General Setup


Create a new analysis directory...
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[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
 [1] "_archived"                                               "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.nb.html"          
 [3] "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rmd"               "2. SNP_analyses.Rmd"                                    
 [5] "20220317.HDAC9.AE_20211201_YAW_SWVANDERLAAN_HDAC9.RData" "20220317.HDAC9.scRNAseq.RData"                          
 [7] "20220318.HDAC9.bulkRNAseq.preparation.RData"             "3.1 bulkRNAseq.preparation.nb.html"                     
 [9] "3.1 bulkRNAseq.preparation.Rmd"                          "3.2 bulkRNAseq.main_analysis.nb.html"                   
[11] "3.2 bulkRNAseq.main_analysis.Rmd"                        "3.3 bulkRNAseq.additional_figures.Rmd"                  
[13] "4. scRNAseq.nb.html"                                     "4. scRNAseq.Rmd"                                        
[15] "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj"                "AnalysisPlan"                                           
[17] "HDAC9"                                                   "images"                                                 
[19] "LICENSE"                                                 "README.html"                                            
[21] "README.md"                                               "references.bib"                                         
[23] "renv"                                                    "renv.lock"                                              
[25] "scripts"                                                 "SNP"                                                    
[27] "targets"                                                
source(paste0(PROJECT_loc, "/scripts/functions.R"))
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")
Using github PAT from envvar GITHUB_PAT
Skipping install of 'patchwork' from a github remote, the SHA1 (79223d30) has not changed since last install.
  Use `force = TRUE` to force installation
install.packages.auto("GGally")
install.packages.auto("ggcorrplot")

# for Seurat etc
install.packages.auto("GenomicFeatures")
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
install.packages.auto("DESeq2")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
Loading required package: EnsDb.Hsapiens.v86
Loading required package: ensembldb

Attaching package: 'ensembldb'

The following object is masked from 'package:openxlsx':

    addFilter

The following object is masked from 'package:dplyr':

    filter

The following object is masked from 'package:stats':

    filter
install.packages.auto("EnhancedVolcano")

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color                 HEX   (RGB)                                     CHR         MAF/INFO
###---------------------------------------------------------------------------------------
### 1     yellow                #FBB820 (251,184,32)                      =>    1       or 1.0>INFO
### 2     gold                #F59D10 (245,157,16)                    =>    2       
### 3     salmon                #E55738 (229,87,56)                   =>    3       or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4     darkpink          #DB003F ((219,0,63)                   =>    4       
### 5     lightpink         #E35493 (227,84,147)                      =>    5       or 0.8<INFO<1.0
### 6     pink                #D5267B (213,38,123)                    =>    6       
### 7     hardpink          #CC0071 (204,0,113)                   =>    7       
### 8     lightpurple       #A8448A (168,68,138)                      =>    8       
### 9     purple                #9A3480 (154,52,128)                      =>    9       
### 10  lavendel            #8D5B9A (141,91,154)                      =>    10      
### 11  bluepurple        #705296 (112,82,150)                    =>    11      
### 12  purpleblue        #686AA9 (104,106,169)               =>    12      
### 13  lightpurpleblue #6173AD (97,115,173/101,120,180)    =>  13      
### 14  seablue             #4C81BF (76,129,191)                      =>    14      
### 15  skyblue             #2F8BC9 (47,139,201)                      =>    15      
### 16  azurblue            #1290D9 (18,144,217)                      =>    16      or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17  lightazurblue     #1396D8 (19,150,216)                    =>    17      
### 18  greenblue           #15A6C1 (21,166,193)                      =>    18      
### 19  seaweedgreen      #5EB17F (94,177,127)                    =>    19      
### 20  yellowgreen       #86B833 (134,184,51)                    =>    20      
### 21  lightmossgreen  #C5D220 (197,210,32)                      =>    21      
### 22  mossgreen           #9FC228 (159,194,40)                      =>    22      or MAF>0.20 or 0.6<INFO<0.8
### 23  lightgreen      #78B113 (120,177,19)                      =>    23/X
### 24  green                 #49A01D (73,160,29)                     =>    24/Y
### 25  grey                  #595A5C (89,90,92)                        =>  25/XY   or MAF<0.01 or 0.0<INFO<0.2
### 26  lightgrey           #A2A3A4 (162,163,164)                 =>    26/MT
###
### ADDITIONAL COLORS
### 27  midgrey         #D7D8D7
### 28  verylightgrey   #ECECEC"
### 29  white           #FFFFFF
### 30  black           #000000
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uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
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                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------

Main analyses

Here we perform the main analyses. We first load the prepared RNAseq data, and extract only the relevant genes.

Targets

Here we obtain data from the HDAC9 in plaques.

library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  

Loading data

We simply load the previously saved RDS-file and extract the clinical and RNAseq data from that.

AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)
[1] "ae1" "ae2" "ae3" "ae4" "ae5" "ae6"
AERNASE <- readRDS(file = paste0(OUT_loc, "/20220318.AERNA.CEA.611pts.SE.after_qc.IC_academic.RDS"))
AERNASE.clin <- as_tibble(colData(AERNASE))

AERNASE.hdac9 <- subset(AERNASE, subset = (rowRanges(AERNASE)$symbol %in% target_genes))

row.names(AERNASE.hdac9) <- rowData(AERNASE.hdac9)$symbol
temp <- as_tibble(t(assay(AERNASE.hdac9)), rownames = "STUDY_NUMBER")

AERNASE.clin.hdac9 <- as.data.frame(merge(AERNASE.clin, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE))
rm(temp)

Getting some summary statistics.

cat("Average expression of a random selection of 1000 genes.\n")
Average expression of a random selection of 1000 genes.
set.seed(141619)
mean(rowMeans(as_tibble(assay(AERNASE)) %>% dplyr::sample_n(1000)))
[1] 12.50463
cat("\nAverage expression of target genes.\n")

Average expression of target genes.
rowMeans(assay(AERNASE.hdac9))
     HDAC9     TWIST1       IL1B        IL6 
28.2062193  5.5859247  5.6072013  0.4157119 
cat("\nGene information.\n")

Gene information.
rowRanges(AERNASE.hdac9)
GRanges object with 4 ranges and 3 metadata columns:
         seqnames              ranges strand |      feature_id      symbol GENEBIOTYPE_EnsDb86
            <Rle>           <IRanges>  <Rle> |     <character> <character>         <character>
   HDAC9        7   18086949-19002416      + | ENSG00000048052       HDAC9      protein_coding
  TWIST1        7   19020991-19117636      - | ENSG00000122691      TWIST1      protein_coding
    IL1B        2 112829751-112836816      - | ENSG00000125538        IL1B      protein_coding
     IL6        7   22725884-22732002      + | ENSG00000136244         IL6      protein_coding
  -------
  seqinfo: 338 sequences from an unspecified genome; no seqlengths
AERNASE.hdac9.genedata <- as_tibble(rowRanges(AERNASE.hdac9))

AERNASE.hdac9.genedata

Analyses

The analyses are focused on three elements:

  1. plaque vulnerability phenotypes
  2. clinical status at inclusion (symptoms)
  3. secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

  1. Age (continuous in 1-year increment). [Age]
  2. Sex (male vs. female). [Gender]
  3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
  4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [DiabetesStatus]
  5. Smoking (current, ex-, never). [SmokerStatus]
  6. LDL-C levels (continuous). [LDL_final]
  7. Use of lipid-lowering drugs. [Med.Statin.LLD]
  8. Use of antiplatelet drugs. [Med.all.antiplatelet]
  9. eGFR (continuous). [GFR_MDRD]
  10. BMI (continuous). [BMI]
  11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combination of [CAD_history, Stroke_history, Peripheral.interv]
  12. Level of stenosis (50-70% vs. 70-99%). [stenose]
  13. Year of surgery [ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

Models

We will analyze the data through four different models

  • Model 1: adjusted for age, sex, and year of surgery
  • Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

  • Percentage of macrophages (continuous trait)
  • Percentage of SMCs (continuous trait)
  • Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
  • Presence of moderate/heavy calcifications (binary trait)
  • Presence of moderate/heavy collagen content (binary trait)
  • Presence of lipid core no/<10% vs. >10% (binary trait)
  • Presence of intraplaque hemorrhage (binary trait)

Quantitative traits

We inspect the plaque characteristics, and inverse-rank normal transformation continuous phenotypes.


# macrophages
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$MAC_rankNorm)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.0955 -0.3800  0.3243  0.2763  0.9551  2.6791      15 
min_macmean <- min(AERNASE.clin.hdac9$MAC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

Minimum value % macrophages: -2.09545532179422.
ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 15 rows containing non-finite values (stat_bin).

# smooth muscle cells
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$SMC_rankNorm)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-2.64518 -0.66839 -0.04095 -0.04085  0.63912  2.55899       17 
min_smcmean <- min(AERNASE.clin.hdac9$SMC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

Minimum value % smooth muscle cells: -2.64517564577702.
ggpubr::gghistogram(AERNASE.clin.hdac9, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 17 rows containing non-finite values (stat_bin).

# vessel density
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$VesselDensity_rankNorm)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-1.9203 -0.4576  0.1109  0.1387  0.7694  3.1703      53 
min_vesseldensity <- min(AERNASE.clin.hdac9$VesselDensity_rankNorm, na.rm = TRUE)
min_vesseldensity
[1] -1.920282
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

Minimum value number of intraplaque neovessels per 3-4 hotspots: -1.92028163343836.
ggpubr::gghistogram(AERNASE.clin.hdac9, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 53 rows containing non-finite values (stat_bin).

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed (‘unstable’) as compared to ‘stable’.


AERNASE.clin.hdac9$MAC_SMC_ratio <- AERNASE.clin.hdac9$MAC_rankNorm / AERNASE.clin.hdac9$SMC_rankNorm

AERNASE.clin.hdac9$MAC_SMC_ratio_rank  <- qnorm((rank(AERNASE.clin.hdac9$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9$MAC_SMC_ratio)))


cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$MAC_rankNorm)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.0955 -0.3800  0.3243  0.2763  0.9551  2.6791      15 
summary(AERNASE.clin.hdac9$SMC_rankNorm)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-2.64518 -0.66839 -0.04095 -0.04085  0.63912  2.55899       17 
summary(AERNASE.clin.hdac9$MAC_SMC_ratio_rank)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-3.1410 -0.6732  0.0000  0.0000  0.6732  3.1410      17 
ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Warning: Removed 17 rows containing non-finite values (stat_bin).
Warning: call dbDisconnect() when finished working with a connection

Binary traits

library(dplyr)
# calcification
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Calc.bin)
      no/minor moderate/heavy           NA's 
           283            320              8 
contrasts(AERNASE.clin.hdac9$Calc.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$CalcificationPlaque <- as.factor(AERNASE.clin.hdac9$Calc.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CalcificationPlaque)) %>%
  dplyr::group_by(Gender, CalcificationPlaque) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Collagen.bin)
      no/minor moderate/heavy           NA's 
           117            485              9 
contrasts(AERNASE.clin.hdac9$Collagen.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$CollagenPlaque <- as.factor(AERNASE.clin.hdac9$Collagen.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CollagenPlaque)) %>%
  dplyr::group_by(Gender, CollagenPlaque) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Fat.bin_10)
 <10%  >10%  NA's 
  143   460     8 
contrasts(AERNASE.clin.hdac9$Fat.bin_10)
       >10%
 <10%     0
 >10%     1
AERNASE.clin.hdac9$Fat10Perc <- as.factor(AERNASE.clin.hdac9$Fat.bin_10)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(Fat10Perc)) %>%
  dplyr::group_by(Gender, Fat10Perc) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# macrophages binned
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$Macrophages.bin)
      no/minor moderate/heavy           NA's 
           261            339             11 
contrasts(AERNASE.clin.hdac9$Macrophages.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$MAC_binned <- as.factor(AERNASE.clin.hdac9$Macrophages.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(MAC_binned)) %>%
  dplyr::group_by(Gender, MAC_binned) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())

rm(df)



# SMC binned
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$SMC.bin)
      no/minor moderate/heavy           NA's 
           193            407             11 
contrasts(AERNASE.clin.hdac9$SMC.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AERNASE.clin.hdac9$SMC_binned <- as.factor(AERNASE.clin.hdac9$SMC.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(SMC_binned)) %>%
  dplyr::group_by(Gender, SMC_binned) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())

rm(df)




# IPH
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$IPH.bin)
  no  yes NA's 
 230  372    9 
contrasts(AERNASE.clin.hdac9$IPH.bin)
    yes
no    0
yes   1
AERNASE.clin.hdac9$IPH <- as.factor(AERNASE.clin.hdac9$IPH.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(IPH)) %>%
  dplyr::group_by(Gender, IPH) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")
Summary of data.
summary(AERNASE.clin.hdac9$AsymptSympt)
Ocular and others       Symptomatic 
              184               427 
contrasts(AERNASE.clin.hdac9$AsymptSympt)
                  Symptomatic
Ocular and others           0
Symptomatic                 1
AERNASE.clin.hdac9$AsymptSympt <- as.factor(AERNASE.clin.hdac9$AsymptSympt)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(AsymptSympt)) %>%
  dplyr::group_by(Gender, AsymptSympt) %>%
dplyr::summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

Correlations between HDAC9 plaque levels and surgery year

Here we compare the HDAC9 plaque gene expression levels.

p1 <- ggpubr::ggscatter(AERNASE.clin.hdac9, 
                        x = "ORyear", 
                        y = "HDAC9",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman", 
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "HDAC9 (normalized expression)",
                        ggtheme = theme_minimal())

p1 
`geom_smooth()` using formula 'y ~ x'

rm(p1)

Prepare modeling

In this section we make some variables to assist with analysis.

We fix the diabetes status variable.


# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"DiabetesStatus"] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == -999] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == 1] <- "Control (no Diabetes Dx/Med)"
AEDB.CEA$DiabetesStatus[DM.composite == 2] <- "Diabetes"
detach(AEDB.CEA)

table(AEDB.CEA$DM.composite, AEDB.CEA$DiabetesStatus)
   
    Control (no Diabetes Dx/Med) Diabetes
  1                         1900        0
  2                            0      596
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$DiabetesStatus <- to_factor(AEDB.CEA.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv

# AEDB.CEA$CAD_history
# AEDB.CEA$Stroke_history
# AEDB.CEA$Peripheral.interv

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"MedHx_CVD"] <- NA
AEDB.CEA$MedHx_CVD[CAD_history == "No history CAD" | Stroke_history == 1 | Peripheral.interv == "no"] <- "No"
AEDB.CEA$MedHx_CVD[CAD_history == "History CAD" | Stroke_history == 2 | Peripheral.interv == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$CAD_history)

No history CAD    History CAD 
          1703            791 
table(AEDB.CEA$Stroke_history)

   1    2 
1691  821 
table(AEDB.CEA$Peripheral.interv)

  no  yes 
1971  515 
table(AEDB.CEA$MedHx_CVD)

  No  yes 
 909 1603 
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$AlcoholUse <- to_factor(AEDB.CEA.temp$AlcoholUse)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)
AERNASE.clin.hdac9$DiabetesStatus <- NULL
AERNASE.clin.hdac9$MedHx_CVD <- NULL

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "dateok", "DiabetesStatus", "MedHx_CVD")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.hdac9$MedHx_CVD <- as_factor(AERNASE.clin.hdac9$MedHx_CVD)
AERNASE.clin.hdac9$DiabetesStatus <- as_factor(AERNASE.clin.hdac9$DiabetesStatus)

study.samplesize = nrow(AERNASE.clin.hdac9) # study.samplesize is an expected variable in the GLM.BIN() GLM.CON() functions

TRAITS.TARGET.RANK = c("HDAC9")

TRAITS.CON.RANK = c("MAC_rankNorm", "SMC_rankNorm", "MAC_SMC_ratio_rank", "VesselDensity_rankNorm")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AERNASE.clin.hdac9$ORdate_epoch <- as.numeric(AERNASE.clin.hdac9$dateok)
AERNASE.clin.hdac9$ORdate_year <- AERNASE.clin.hdac9$ORyear

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`
summary(AERNASE.clin.hdac9$ORdate_epoch)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
1.324e+10 1.332e+10 1.338e+10 1.339e+10 1.346e+10 1.367e+10 
cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")

Summary of 'year of surgery' in 'years' (); coded as `factor()`
table(AERNASE.clin.hdac9$ORdate_year)

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 
  32   61   65   80   81   64   62   67   34   31   22    5    3    3    1    0    0    0    0    0    0 
# COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")
str(AERNASE.clin.hdac9)
'data.frame':   611 obs. of  74 variables:
 $ STUDY_NUMBER               : chr  "ae1" "ae1026" "ae1029" "ae1032" ...
 $ SampleType                 : chr  "plaque" "plaque" "plaque" "plaque" ...
 $ RNAseqType                 : chr  "3' RNAseq" "3' RNAseq" "3' RNAseq" "3' RNAseq" ...
 $ Hospital                   : Factor w/ 2 levels "St. Antonius, Nieuwegein",..: 1 2 2 2 2 2 2 1 2 2 ...
 $ ORyear                     : Factor w/ 21 levels "2002","2003",..: 1 4 4 4 4 4 5 2 4 6 ...
 $ Artery_summary             : Factor w/ 9 levels "No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Age                        : num  29 46 23 15 41 37 43 44 26 25 ...
 $ Gender                     : Factor w/ 2 levels "female","male": 2 2 2 1 1 1 2 2 2 1 ...
 $ TC_final                   : num  4.48 NA 4.2 NA 2.92 ...
 $ LDL_final                  : num  2.94 NA NA NA 0.65 ...
 $ HDL_final                  : num  1 NA NA NA 0.87 ...
 $ TG_final                   : num  1.21 NA NA NA 3.07 ...
 $ systolic                   : num  129 71 61 81 15 91 71 76 51 61 ...
 $ diastoli                   : num  51 41 44 51 16 51 51 31 21 13 ...
 $ GFR_MDRD                   : num  491 951 1499 2580 135 ...
 $ BMI                        : num  879 682 208 245 1048 ...
 $ KDOQI                      : Factor w/ 5 levels "Normal kidney function",..: 3 2 2 1 3 4 2 3 3 3 ...
 $ BMI_WHO                    : Factor w/ 4 levels "Underweight",..: 3 3 2 2 4 3 3 2 3 2 ...
 $ SmokerStatus               : Factor w/ 3 levels "Current smoker",..: 2 NA 1 1 3 1 3 3 3 1 ...
 $ AlcoholUse                 : Factor w/ 1 level "Yes": 1 NA 1 NA NA 1 NA 1 NA 1 ...
 $ Hypertension.selfreport    : Factor w/ 2 levels "no","yes": 2 1 1 1 2 2 2 2 2 2 ...
 $ Hypertension.selfreportdrug: Factor w/ 2 levels "no","yes": 2 1 1 1 2 1 2 2 2 2 ...
 $ Hypertension.composite     : Factor w/ 2 levels "no","yes": 2 2 1 1 2 2 2 2 2 2 ...
 $ Hypertension.drugs         : Factor w/ 2 levels "no","yes": 1 2 1 1 2 2 2 2 2 2 ...
 $ Med.anticoagulants         : Factor w/ 2 levels "no","yes": 1 2 1 1 1 2 1 2 1 1 ...
 $ Med.all.antiplatelet       : Factor w/ 2 levels "no","yes": 2 1 2 2 2 1 2 2 2 2 ...
 $ Med.Statin.LLD             : Factor w/ 2 levels "no","yes": 1 2 2 1 2 2 2 1 2 2 ...
 $ Stroke_Dx                  : Factor w/ 2 levels "No stroke diagnosed",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ sympt                      : Factor w/ 17 levels "Asymptomatic",..: 2 2 4 2 5 3 1 1 1 1 ...
 $ Symptoms.5G                : Factor w/ 5 levels "Ocular","Other",..: 4 4 1 4 2 4 5 5 5 5 ...
 $ AsymptSympt                : Factor w/ 2 levels "Ocular and others",..: 2 2 1 2 1 2 2 2 2 2 ...
 $ AsymptSympt2G              : Factor w/ 1 level "Symptomatic": 1 1 1 1 1 1 1 1 1 1 ...
 $ Symptoms.Update2G          : Factor w/ 1 level "Symptomatic": 1 1 1 1 1 1 1 1 1 1 ...
 $ Symptoms.Update3G          : Factor w/ 1 level "Symptomatic": 1 1 1 1 1 1 1 1 1 1 ...
 $ restenos                   : Factor w/ 3 levels "de novo","restenosis",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ stenose                    : Factor w/ 9 levels "0-49%","50-70%",..: 4 2 3 3 4 3 3 4 3 3 ...
 $ CAD_history                : Factor w/ 2 levels "No history CAD",..: 2 1 1 1 1 1 1 1 1 1 ...
 $ PAOD                       : Factor w/ 2 levels "no","yes": 2 2 1 1 1 1 1 1 1 2 ...
 $ Peripheral.interv          : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 2 ...
 $ EP_composite               : Factor w/ 2 levels "No composite endpoints",..: 1 2 2 1 2 2 2 1 1 1 ...
 $ EP_composite_time          : num  12.258 0.208 0.685 1.288 2.603 ...
 $ EP_major                   : Factor w/ 2 levels "No major events (endpoints)",..: 1 1 1 1 2 2 2 1 1 1 ...
 $ EP_major_time              : num  12.258 0.647 2.989 1.288 2.603 ...
 $ MAC_rankNorm               : num  -0.0697 -0.7511 0.9114 -0.38 -1.0968 ...
 $ SMC_rankNorm               : num  1.522 0.58 0.673 1.256 0.342 ...
 $ Macrophages.bin            : Factor w/ 2 levels "no/minor","moderate/heavy": 2 1 2 1 1 2 2 1 2 1 ...
 $ SMC.bin                    : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ Neutrophils_rankNorm       : num  NA 0.591 0.118 -1.618 -1.618 ...
 $ MastCells_rankNorm         : num  NA 0.633 NA NA NA ...
 $ IPH.bin                    : Factor w/ 2 levels "no","yes": 1 2 2 1 1 1 1 2 1 1 ...
 $ VesselDensity_rankNorm     : num  -0.723 0.769 0.854 1.14 -0.861 ...
 $ Calc.bin                   : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 1 1 1 2 2 2 1 ...
 $ Collagen.bin               : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ Fat.bin_10                 : Factor w/ 2 levels " <10%"," >10%": 2 2 2 2 1 1 2 1 2 1 ...
 $ Fat.bin_40                 : Factor w/ 2 levels "<40%",">40%": 1 1 1 1 1 1 2 1 1 1 ...
 $ OverallPlaquePhenotype     : Factor w/ 3 levels "atheromatous",..: 1 2 1 1 2 2 NA 2 1 2 ...
 $ Plaque_Vulnerability_Index : Factor w/ 6 levels "0","1","2","3",..: 3 3 4 2 1 2 5 2 3 1 ...
 $ HDAC9                      : int  24 11 7 5 13 46 14 0 30 12 ...
 $ TWIST1                     : int  1 13 5 0 1 1 3 1 19 2 ...
 $ IL1B                       : int  2 4 2 1 1 2 5 2 43 2 ...
 $ IL6                        : int  0 1 0 0 0 0 0 0 5 0 ...
 $ MAC_SMC_ratio              : num  -0.0458 -1.2947 1.3537 -0.3026 -3.2041 ...
 $ MAC_SMC_ratio_rank         : num  -0.215 -0.88 0.728 -0.36 -1.31 ...
 $ CalcificationPlaque        : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 1 1 1 2 2 2 1 ...
 $ CollagenPlaque             : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ Fat10Perc                  : Factor w/ 2 levels " <10%"," >10%": 2 2 2 2 1 1 2 1 2 1 ...
 $ MAC_binned                 : Factor w/ 2 levels "no/minor","moderate/heavy": 2 1 2 1 1 2 2 1 2 1 ...
 $ SMC_binned                 : Factor w/ 2 levels "no/minor","moderate/heavy": 2 2 2 2 2 2 1 2 2 2 ...
 $ IPH                        : Factor w/ 2 levels "no","yes": 1 2 2 1 1 1 1 2 1 1 ...
 $ dateok                     : num  1.32e+10 1.33e+10 1.33e+10 1.33e+10 1.33e+10 ...
 $ DiabetesStatus             : Factor w/ 2 levels "Control (no Diabetes Dx/Med)",..: 1 1 1 1 2 1 1 1 1 1 ...
 $ MedHx_CVD                  : Factor w/ 2 levels "yes","No": 1 2 1 2 2 1 2 2 2 1 ...
 $ ORdate_epoch               : num  1.32e+10 1.33e+10 1.33e+10 1.33e+10 1.33e+10 ...
 $ ORdate_year                : Factor w/ 21 levels "2002","2003",..: 1 4 4 4 4 4 5 2 4 6 ...
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

Analysis of continuous/quantitative plaque traits as a function of plaque HDAC9 expression levels.

library(dplyr)
library(MASS)

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))

cat("Running linear regression...\n")
Running linear regression...
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing MAC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      28.22  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-29.01 -17.56  -7.40   8.71 420.59 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.289e+02  2.147e+02  -0.600    0.549
currentDF[, TRAIT]  2.065e-01  1.399e+00   0.148    0.883
Age                 1.415e-01  1.500e-01   0.943    0.346
Gendermale         -1.241e+00  3.096e+00  -0.401    0.689
ORdate_epoch        1.144e-08  1.607e-08   0.712    0.477

Residual standard error: 32.39 on 591 degrees of freedom
Multiple R-squared:  0.002791,  Adjusted R-squared:  -0.003959 
F-statistic: 0.4135 on 4 and 591 DF,  p-value: 0.799

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.206547 
Standard error............: 1.398569 
Odds ratio (effect size)..: 1.229 
Lower 95% CI..............: 0.079 
Upper 95% CI..............: 19.063 
T-value...................: 0.147684 
P-value...................: 0.8826422 
R^2.......................: 0.002791 
Adjusted r^2..............: -0.003959 
Sample size of AE DB......: 611 
Sample size of model......: 596 
Missing data %............: 2.454992 

- processing SMC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            27.975              -4.619  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-34.98 -17.20  -7.50   7.82 420.01 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)   
(Intercept)        -2.827e+01  2.148e+02  -0.132  0.89536   
currentDF[, TRAIT] -4.555e+00  1.471e+00  -3.097  0.00205 **
Age                 6.111e-02  1.508e-01   0.405  0.68556   
Gendermale         -2.257e+00  3.090e+00  -0.730  0.46549   
ORdate_epoch        4.172e-09  1.608e-08   0.260  0.79533   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 32.16 on 589 degrees of freedom
Multiple R-squared:  0.01891,   Adjusted R-squared:  0.01225 
F-statistic: 2.838 on 4 and 589 DF,  p-value: 0.02377

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -4.554888 
Standard error............: 1.470541 
Odds ratio (effect size)..: 0.011 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 0.188 
T-value...................: -3.097424 
P-value...................: 0.002045031 
R^2.......................: 0.018908 
Adjusted r^2..............: 0.012245 
Sample size of AE DB......: 611 
Sample size of model......: 594 
Missing data %............: 2.782324 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            28.163              -2.839  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-33.32 -17.33  -7.61   7.49 418.30 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.661e+02  2.131e+02  -0.779   0.4361  
currentDF[, TRAIT] -2.929e+00  1.330e+00  -2.202   0.0281 *
Age                 1.336e-01  1.494e-01   0.895   0.3713  
Gendermale         -1.655e+00  3.091e+00  -0.535   0.5926  
ORdate_epoch        1.426e-08  1.596e-08   0.893   0.3720  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 32.29 on 589 degrees of freedom
Multiple R-squared:  0.01107,   Adjusted R-squared:  0.004349 
F-statistic: 1.648 on 4 and 589 DF,  p-value: 0.1607

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -2.928947 
Standard error............: 1.33041 
Odds ratio (effect size)..: 0.053 
Lower 95% CI..............: 0.004 
Upper 95% CI..............: 0.725 
T-value...................: -2.201537 
P-value...................: 0.02808507 
R^2.......................: 0.011065 
Adjusted r^2..............: 0.004349 
Sample size of AE DB......: 611 
Sample size of model......: 594 
Missing data %............: 2.782324 

- processing VesselDensity_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      28.58  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-29.63 -17.87  -7.71   8.54 420.16 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.649e+02  2.272e+02  -0.726    0.468
currentDF[, TRAIT] -5.828e-01  1.485e+00  -0.392    0.695
Age                 1.461e-01  1.583e-01   0.923    0.356
Gendermale         -1.405e+00  3.260e+00  -0.431    0.667
ORdate_epoch        1.417e-08  1.701e-08   0.833    0.405

Residual standard error: 33.08 on 553 degrees of freedom
Multiple R-squared:  0.003666,  Adjusted R-squared:  -0.003541 
F-statistic: 0.5087 on 4 and 553 DF,  p-value: 0.7294

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.582804 
Standard error............: 1.485197 
Odds ratio (effect size)..: 0.558 
Lower 95% CI..............: 0.03 
Upper 95% CI..............: 10.259 
T-value...................: -0.392408 
P-value...................: 0.6949077 
R^2.......................: 0.003666 
Adjusted r^2..............: -0.003541 
Sample size of AE DB......: 611 
Sample size of model......: 558 
Missing data %............: 8.674304 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque HDAC9 expression levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year,
    #           data  =  currentDF, family = binomial(link = "logit"))
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch,
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale         ORdate_epoch  
          9.089e+01            5.593e-03           -3.617e-01           -6.770e-09  

Degrees of Freedom: 602 Total (i.e. Null);  599 Residual
Null Deviance:      833.7 
Residual Deviance: 780.8    AIC: 788.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.856  -1.115   0.768   1.052   1.710  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          9.127e+01  1.403e+01   6.506 7.71e-11 ***
currentDF[, TARGET]  5.549e-03  3.146e-03   1.764   0.0778 .  
Age                  4.413e-03  9.607e-03   0.459   0.6460    
Gendermale          -3.612e-01  2.012e-01  -1.796   0.0725 .  
ORdate_epoch        -6.811e-09  1.050e-09  -6.485 8.88e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 833.66  on 602  degrees of freedom
Residual deviance: 780.57  on 598  degrees of freedom
AIC: 790.57

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.005549 
Standard error............: 0.003146 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 0.999 
Upper 95% CI..............: 1.012 
Z-value...................: 1.763675 
P-value...................: 0.07778677 
Hosmer and Lemeshow r^2...: 0.063691 
Cox and Snell r^2.........: 0.084289 
Nagelkerke's pseudo r^2...: 0.112527 
Sample size of AE DB......: 611 
Sample size of model......: 603 
Missing data %............: 1.309329 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.422  

Degrees of Freedom: 601 Total (i.e. Null);  601 Residual
Null Deviance:      592.9 
Residual Deviance: 592.9    AIC: 594.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9490   0.5987   0.6368   0.6722   1.1578  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)
(Intercept)          1.634e+01  1.615e+01   1.012    0.312
currentDF[, TARGET] -3.491e-03  2.812e-03  -1.242    0.214
Age                  4.386e-03  1.162e-02   0.378    0.706
Gendermale           1.249e-01  2.380e-01   0.525    0.600
ORdate_epoch        -1.125e-09  1.208e-09  -0.931    0.352

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 592.94  on 601  degrees of freedom
Residual deviance: 590.28  on 597  degrees of freedom
AIC: 600.28

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.003491 
Standard error............: 0.002812 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.002 
Z-value...................: -1.241602 
P-value...................: 0.2143835 
Hosmer and Lemeshow r^2...: 0.004473 
Cox and Snell r^2.........: 0.004396 
Nagelkerke's pseudo r^2...: 0.007016 
Sample size of AE DB......: 611 
Sample size of model......: 602 
Missing data %............: 1.472995 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale         ORdate_epoch  
          8.888e+01            1.881e-02            7.717e-01           -6.623e-09  

Degrees of Freedom: 602 Total (i.e. Null);  599 Residual
Null Deviance:      660.6 
Residual Deviance: 607.9    AIC: 615.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5116   0.2827   0.5812   0.7565   1.3154  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          8.998e+01  1.609e+01   5.593 2.24e-08 ***
currentDF[, TARGET]  1.866e-02  5.470e-03   3.412 0.000644 ***
Age                  1.226e-02  1.109e-02   1.106 0.268904    
Gendermale           7.758e-01  2.237e-01   3.468 0.000524 ***
ORdate_epoch        -6.736e-09  1.205e-09  -5.592 2.25e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 660.61  on 602  degrees of freedom
Residual deviance: 606.67  on 598  degrees of freedom
AIC: 616.67

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.018664 
Standard error............: 0.00547 
Odds ratio (effect size)..: 1.019 
Lower 95% CI..............: 1.008 
Upper 95% CI..............: 1.03 
Z-value...................: 3.412234 
P-value...................: 0.0006443268 
Hosmer and Lemeshow r^2...: 0.081656 
Cox and Snell r^2.........: 0.085572 
Nagelkerke's pseudo r^2...: 0.128556 
Sample size of AE DB......: 611 
Sample size of model......: 603 
Missing data %............: 1.309329 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_epoch, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)    Gendermale  ORdate_epoch  
   8.801e+01     6.163e-01    -6.570e-09  

Degrees of Freedom: 601 Total (i.e. Null);  599 Residual
Null Deviance:      800.7 
Residual Deviance: 752.1    AIC: 758.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9201  -1.1952   0.7258   0.9514   1.7063  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          8.891e+01  1.419e+01   6.267 3.69e-10 ***
currentDF[, TARGET]  7.551e-04  2.829e-03   0.267  0.78956    
Age                  8.173e-03  9.843e-03   0.830  0.40631    
Gendermale           6.192e-01  2.010e-01   3.081  0.00207 ** 
ORdate_epoch        -6.660e-09  1.063e-09  -6.267 3.67e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 800.74  on 601  degrees of freedom
Residual deviance: 751.31  on 597  degrees of freedom
AIC: 761.31

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: IPH 
Effect size...............: 0.000755 
Standard error............: 0.002829 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.995 
Upper 95% CI..............: 1.006 
Z-value...................: 0.266882 
P-value...................: 0.7895597 
Hosmer and Lemeshow r^2...: 0.061732 
Cox and Snell r^2.........: 0.078831 
Nagelkerke's pseudo r^2...: 0.107171 
Sample size of AE DB......: 611 
Sample size of model......: 602 
Missing data %............: 1.472995 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
   -0.09663      0.47469  

Degrees of Freedom: 599 Total (i.e. Null);  598 Residual
Null Deviance:      821.6 
Residual Deviance: 815.5    AIC: 819.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7639  -1.2859   0.9725   1.0439   1.3126  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          1.197e+01  1.303e+01   0.919   0.3582  
currentDF[, TARGET]  3.936e-03  2.974e-03   1.324   0.1856  
Age                  1.141e-03  9.342e-03   0.122   0.9028  
Gendermale           4.921e-01  1.926e-01   2.555   0.0106 *
ORdate_epoch        -9.135e-10  9.757e-10  -0.936   0.3491  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 821.61  on 599  degrees of freedom
Residual deviance: 812.70  on 595  degrees of freedom
AIC: 822.7

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.003936 
Standard error............: 0.002974 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 1.01 
Z-value...................: 1.323626 
P-value...................: 0.1856274 
Hosmer and Lemeshow r^2...: 0.010844 
Cox and Snell r^2.........: 0.01474 
Nagelkerke's pseudo r^2...: 0.019766 
Sample size of AE DB......: 611 
Sample size of model......: 600 
Missing data %............: 1.800327 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age, family = binomial(link = "logit"), data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age  
           1.677785            -0.004282            -0.023224  

Degrees of Freedom: 599 Total (i.e. Null);  597 Residual
Null Deviance:      753.7 
Residual Deviance: 745.4    AIC: 751.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8005  -1.3987   0.8089   0.8977   1.7277  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          5.695e+00  1.383e+01   0.412   0.6805  
currentDF[, TARGET] -4.322e-03  2.741e-03  -1.577   0.1149  
Age                 -2.301e-02  1.013e-02  -2.272   0.0231 *
Gendermale          -2.441e-01  2.111e-01  -1.156   0.2475  
ORdate_epoch        -2.865e-10  1.036e-09  -0.277   0.7821  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 753.74  on 599  degrees of freedom
Residual deviance: 743.90  on 595  degrees of freedom
AIC: 753.9

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.004322 
Standard error............: 0.002741 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.99 
Upper 95% CI..............: 1.001 
Z-value...................: -1.576594 
P-value...................: 0.114889 
Hosmer and Lemeshow r^2...: 0.013058 
Cox and Snell r^2.........: 0.01627 
Nagelkerke's pseudo r^2...: 0.022747 
Sample size of AE DB......: 611 
Sample size of model......: 600 
Missing data %............: 1.800327 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque HDAC9 expression levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
              Hypertension.composite + DiabetesStatus + SmokerStatus + 
              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
              MedHx_CVD + stenose, 
              data = currentDF) 
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing MAC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      28.44  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-33.50 -17.49  -6.78   7.74 414.80 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.492e+02  2.525e+02  -0.591    0.555
currentDF[, TRAIT]        -3.679e-01  1.588e+00  -0.232    0.817
Age                        1.993e-01  1.970e-01   1.011    0.312
Gendermale                 1.233e+00  3.594e+00   0.343    0.732
ORdate_epoch               1.175e-08  1.880e-08   0.625    0.532
Hypertension.compositeyes  2.040e+00  4.540e+00   0.449    0.653
DiabetesStatusDiabetes    -5.176e+00  3.734e+00  -1.386    0.166
SmokerStatusEx-smoker     -1.015e-02  3.417e+00  -0.003    0.998
SmokerStatusNever smoked  -2.624e+00  4.481e+00  -0.586    0.558
Med.Statin.LLDyes          7.455e-02  3.631e+00   0.021    0.984
Med.all.antiplateletyes    4.478e+00  5.154e+00   0.869    0.385
GFR_MDRD                  -8.473e-04  2.458e-03  -0.345    0.730
BMI                       -1.084e-03  5.151e-03  -0.210    0.833
MedHx_CVDNo                7.364e-01  3.140e+00   0.235    0.815
stenose50-70%              1.311e+01  2.467e+01   0.531    0.595
stenose70-90%              7.327e+00  2.400e+01   0.305    0.760
stenose90-99%              1.198e+01  2.402e+01   0.499    0.618
stenose100% (Occlusion)    7.806e+00  2.946e+01   0.265    0.791

Residual standard error: 33.53 on 502 degrees of freedom
Multiple R-squared:  0.01664,   Adjusted R-squared:  -0.01666 
F-statistic: 0.4998 on 17 and 502 DF,  p-value: 0.9533

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.36787 
Standard error............: 1.588445 
Odds ratio (effect size)..: 0.692 
Lower 95% CI..............: 0.031 
Upper 95% CI..............: 15.572 
T-value...................: -0.231591 
P-value...................: 0.81695 
R^2.......................: 0.016643 
Adjusted r^2..............: -0.016658 
Sample size of AE DB......: 611 
Sample size of model......: 520 
Missing data %............: 14.89362 

- processing SMC_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            28.147              -5.029  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-40.36 -17.45  -6.66   6.92 413.88 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -5.197e+01  2.507e+02  -0.207  0.83589   
currentDF[, TRAIT]        -4.828e+00  1.641e+00  -2.941  0.00342 **
Age                        1.512e-01  1.963e-01   0.770  0.44157   
Gendermale                -4.477e-01  3.602e+00  -0.124  0.90113   
ORdate_epoch               4.589e-09  1.865e-08   0.246  0.80573   
Hypertension.compositeyes  1.643e+00  4.502e+00   0.365  0.71522   
DiabetesStatusDiabetes    -4.248e+00  3.711e+00  -1.145  0.25288   
SmokerStatusEx-smoker      2.042e-01  3.393e+00   0.060  0.95203   
SmokerStatusNever smoked  -3.202e+00  4.446e+00  -0.720  0.47177   
Med.Statin.LLDyes         -3.433e-01  3.610e+00  -0.095  0.92428   
Med.all.antiplateletyes    4.117e+00  5.113e+00   0.805  0.42113   
GFR_MDRD                  -1.431e-04  2.448e-03  -0.058  0.95338   
BMI                       -1.149e-03  5.106e-03  -0.225  0.82200   
MedHx_CVDNo                1.331e+00  3.121e+00   0.426  0.66996   
stenose50-70%              1.345e+01  2.444e+01   0.551  0.58221   
stenose70-90%              8.150e+00  2.376e+01   0.343  0.73172   
stenose90-99%              1.305e+01  2.380e+01   0.548  0.58373   
stenose100% (Occlusion)    9.146e+00  2.922e+01   0.313  0.75445   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 33.26 on 501 degrees of freedom
Multiple R-squared:  0.0334,    Adjusted R-squared:  0.0005975 
F-statistic: 1.018 on 17 and 501 DF,  p-value: 0.4362

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -4.82779 
Standard error............: 1.641478 
Odds ratio (effect size)..: 0.008 
Lower 95% CI..............: 0 
Upper 95% CI..............: 0.2 
T-value...................: -2.941123 
P-value...................: 0.003421542 
R^2.......................: 0.033396 
Adjusted r^2..............: 0.000597 
Sample size of AE DB......: 611 
Sample size of model......: 519 
Missing data %............: 15.05728 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + DiabetesStatus, 
    data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]  DiabetesStatusDiabetes  
                29.469                  -3.705                  -5.199  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-37.27 -16.42  -6.72   9.08 410.01 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -2.416e+02  2.490e+02  -0.970  0.33232   
currentDF[, TRAIT]        -4.236e+00  1.530e+00  -2.769  0.00583 **
Age                        2.064e-01  1.956e-01   1.055  0.29197   
Gendermale                 6.190e-01  3.570e+00   0.173  0.86240   
ORdate_epoch               1.832e-08  1.851e-08   0.990  0.32273   
Hypertension.compositeyes  2.418e+00  4.508e+00   0.536  0.59187   
DiabetesStatusDiabetes    -5.518e+00  3.708e+00  -1.488  0.13737   
SmokerStatusEx-smoker     -4.992e-01  3.397e+00  -0.147  0.88322   
SmokerStatusNever smoked  -3.748e+00  4.464e+00  -0.840  0.40154   
Med.Statin.LLDyes          2.952e-01  3.607e+00   0.082  0.93481   
Med.all.antiplateletyes    5.344e+00  5.129e+00   1.042  0.29797   
GFR_MDRD                  -8.481e-04  2.441e-03  -0.347  0.72837   
BMI                       -1.841e-03  5.118e-03  -0.360  0.71917   
MedHx_CVDNo                1.086e+00  3.120e+00   0.348  0.72805   
stenose50-70%              1.629e+01  2.449e+01   0.665  0.50626   
stenose70-90%              1.116e+01  2.383e+01   0.468  0.63968   
stenose90-99%              1.656e+01  2.388e+01   0.694  0.48830   
stenose100% (Occlusion)    1.190e+01  2.929e+01   0.406  0.68459   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 33.29 on 501 degrees of freedom
Multiple R-squared:  0.03153,   Adjusted R-squared:  -0.001334 
F-statistic: 0.9594 on 17 and 501 DF,  p-value: 0.5035

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -4.236248 
Standard error............: 1.529904 
Odds ratio (effect size)..: 0.014 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 0.29 
T-value...................: -2.768964 
P-value...................: 0.00583196 
R^2.......................: 0.031528 
Adjusted r^2..............: -0.001334 
Sample size of AE DB......: 611 
Sample size of model......: 519 
Missing data %............: 15.05728 

- processing VesselDensity_rankNorm


Call:
lm(formula = currentDF[, TARGET] ~ DiabetesStatus, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes  
                29.937                  -6.025  


Call:
lm(formula = currentDF[, TARGET] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-34.56 -18.14  -6.57   8.25 414.27 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -2.151e+02  2.678e+02  -0.803    0.422
currentDF[, TRAIT]        -3.175e-01  1.703e+00  -0.186    0.852
Age                        1.910e-01  2.100e-01   0.910    0.363
Gendermale                 1.197e+00  3.788e+00   0.316    0.752
ORdate_epoch               1.689e-08  1.992e-08   0.848    0.397
Hypertension.compositeyes  1.661e-01  4.826e+00   0.034    0.973
DiabetesStatusDiabetes    -6.119e+00  4.085e+00  -1.498    0.135
SmokerStatusEx-smoker     -7.764e-02  3.634e+00  -0.021    0.983
SmokerStatusNever smoked  -2.278e+00  4.813e+00  -0.473    0.636
Med.Statin.LLDyes         -1.437e+00  3.830e+00  -0.375    0.708
Med.all.antiplateletyes    4.744e+00  5.464e+00   0.868    0.386
GFR_MDRD                  -1.282e-03  2.610e-03  -0.491    0.624
BMI                       -2.992e-04  5.456e-03  -0.055    0.956
MedHx_CVDNo                1.580e+00  3.329e+00   0.475    0.635
stenose50-70%              1.230e+01  2.536e+01   0.485    0.628
stenose70-90%              7.525e+00  2.454e+01   0.307    0.759
stenose90-99%              1.221e+01  2.458e+01   0.497    0.620
stenose100% (Occlusion)    7.699e+00  3.017e+01   0.255    0.799
stenose50-99%              2.792e+00  4.273e+01   0.065    0.948

Residual standard error: 34.29 on 468 degrees of freedom
Multiple R-squared:  0.01795,   Adjusted R-squared:  -0.01982 
F-statistic: 0.4752 on 18 and 468 DF,  p-value: 0.9679

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.317474 
Standard error............: 1.702736 
Odds ratio (effect size)..: 0.728 
Lower 95% CI..............: 0.026 
Upper 95% CI..............: 20.489 
T-value...................: -0.186449 
P-value...................: 0.8521732 
R^2.......................: 0.01795 
Adjusted r^2..............: -0.019821 
Sample size of AE DB......: 611 
Sample size of model......: 487 
Missing data %............: 20.2946 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of HDAC9.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch + Hypertension.composite, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)        currentDF[, TARGET]                 Gendermale               ORdate_epoch  Hypertension.compositeyes  
                8.113e+01                  5.333e-03                 -3.216e-01                 -6.080e-09                  4.737e-01  

Degrees of Freedom: 524 Total (i.e. Null);  520 Residual
Null Deviance:      727.5 
Residual Deviance: 685.9    AIC: 695.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.790  -1.082   0.640   1.074   1.760  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                7.375e+01  1.561e+01   4.724 2.32e-06 ***
currentDF[, TARGET]        5.116e-03  3.386e-03   1.511   0.1308    
Age                       -6.373e-03  1.225e-02  -0.520   0.6029    
Gendermale                -3.365e-01  2.242e-01  -1.501   0.1335    
ORdate_epoch              -5.439e-09  1.161e-09  -4.687 2.77e-06 ***
Hypertension.compositeyes  5.208e-01  2.856e-01   1.824   0.0682 .  
DiabetesStatusDiabetes    -2.796e-02  2.303e-01  -0.121   0.9034    
SmokerStatusEx-smoker     -5.353e-02  2.126e-01  -0.252   0.8012    
SmokerStatusNever smoked  -2.635e-01  2.791e-01  -0.944   0.3450    
Med.Statin.LLDyes         -1.876e-01  2.252e-01  -0.833   0.4048    
Med.all.antiplateletyes   -1.568e-01  3.205e-01  -0.489   0.6247    
GFR_MDRD                  -1.717e-04  1.524e-04  -1.127   0.2599    
BMI                       -8.958e-05  3.194e-04  -0.280   0.7792    
MedHx_CVDNo                6.958e-03  1.957e-01   0.036   0.9716    
stenose50-70%             -4.450e-01  1.547e+00  -0.288   0.7737    
stenose70-90%             -5.208e-01  1.504e+00  -0.346   0.7291    
stenose90-99%             -1.987e-01  1.506e+00  -0.132   0.8950    
stenose100% (Occlusion)    6.063e-01  1.914e+00   0.317   0.7515    
stenose50-99%             -1.221e+01  5.354e+02  -0.023   0.9818    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 727.48  on 524  degrees of freedom
Residual deviance: 678.49  on 506  degrees of freedom
AIC: 716.49

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.005116 
Standard error............: 0.003386 
Odds ratio (effect size)..: 1.005 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 1.012 
Z-value...................: 1.51086 
P-value...................: 0.1308242 
Hosmer and Lemeshow r^2...: 0.06734 
Cox and Snell r^2.........: 0.08909 
Nagelkerke's pseudo r^2...: 0.118811 
Sample size of AE DB......: 611 
Sample size of model......: 525 
Missing data %............: 14.07529 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerStatus + 
    Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked   Med.all.antiplateletyes  
                 1.13580                  -0.09493                  -0.68875                   0.56243  

Degrees of Freedom: 523 Total (i.e. Null);  520 Residual
Null Deviance:      507.9 
Residual Deviance: 499.8    AIC: 507.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1792   0.4865   0.5672   0.6577   1.1449  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                3.457e+00  1.013e+03   0.003   0.9973  
currentDF[, TARGET]       -4.143e-03  2.996e-03  -1.383   0.1667  
Age                        1.631e-02  1.513e-02   1.078   0.2812  
Gendermale                -6.460e-02  2.790e-01  -0.232   0.8169  
ORdate_epoch               8.611e-10  1.427e-09   0.603   0.5462  
Hypertension.compositeyes -1.477e-01  3.617e-01  -0.408   0.6830  
DiabetesStatusDiabetes     9.145e-02  2.949e-01   0.310   0.7565  
SmokerStatusEx-smoker     -1.231e-01  2.745e-01  -0.448   0.6539  
SmokerStatusNever smoked  -7.641e-01  3.241e-01  -2.358   0.0184 *
Med.Statin.LLDyes         -1.756e-01  2.857e-01  -0.615   0.5387  
Med.all.antiplateletyes    7.610e-01  3.578e-01   2.127   0.0334 *
GFR_MDRD                   1.717e-05  1.916e-04   0.090   0.9286  
BMI                        2.145e-04  3.983e-04   0.539   0.5901  
MedHx_CVDNo               -7.812e-02  2.431e-01  -0.321   0.7480  
stenose50-70%             -1.511e+01  1.013e+03  -0.015   0.9881  
stenose70-90%             -1.431e+01  1.013e+03  -0.014   0.9887  
stenose90-99%             -1.415e+01  1.013e+03  -0.014   0.9888  
stenose100% (Occlusion)    2.109e-01  1.237e+03   0.000   0.9999  
stenose50-99%             -8.065e-01  1.773e+03   0.000   0.9996  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 507.93  on 523  degrees of freedom
Residual deviance: 488.42  on 505  degrees of freedom
AIC: 526.42

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.004143 
Standard error............: 0.002996 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.99 
Upper 95% CI..............: 1.002 
Z-value...................: -1.382886 
P-value...................: 0.1667 
Hosmer and Lemeshow r^2...: 0.038422 
Cox and Snell r^2.........: 0.036559 
Nagelkerke's pseudo r^2...: 0.058903 
Sample size of AE DB......: 611 
Sample size of model......: 524 
Missing data %............: 14.23895 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Gender + ORdate_epoch + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]           Gendermale         ORdate_epoch                  BMI  
          9.026e+01            1.994e-02            7.199e-01           -6.702e-09           -5.056e-04  

Degrees of Freedom: 524 Total (i.e. Null);  520 Residual
Null Deviance:      580.9 
Residual Deviance: 533.2    AIC: 543.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.57779   0.07006   0.56602   0.75768   1.36946  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.157e+02  6.173e+02   0.187  0.85134    
currentDF[, TARGET]        1.912e-02  6.065e-03   3.152  0.00162 ** 
Age                        1.699e-02  1.443e-02   1.177  0.23903    
Gendermale                 7.617e-01  2.525e-01   3.016  0.00256 ** 
ORdate_epoch              -7.667e-09  1.379e-09  -5.559 2.72e-08 ***
Hypertension.compositeyes -1.967e-02  3.359e-01  -0.059  0.95331    
DiabetesStatusDiabetes    -1.488e-01  2.678e-01  -0.556  0.57853    
SmokerStatusEx-smoker     -1.190e-01  2.513e-01  -0.474  0.63567    
SmokerStatusNever smoked  -1.620e-02  3.301e-01  -0.049  0.96085    
Med.Statin.LLDyes          4.476e-02  2.717e-01   0.165  0.86916    
Med.all.antiplateletyes    4.163e-01  3.614e-01   1.152  0.24930    
GFR_MDRD                   1.224e-04  1.820e-04   0.673  0.50124    
BMI                       -5.080e-04  3.731e-04  -1.362  0.17329    
MedHx_CVDNo                3.757e-02  2.298e-01   0.163  0.87015    
stenose50-70%             -1.266e+01  6.170e+02  -0.021  0.98363    
stenose70-90%             -1.351e+01  6.170e+02  -0.022  0.98254    
stenose90-99%             -1.374e+01  6.170e+02  -0.022  0.98224    
stenose100% (Occlusion)   -1.429e+01  6.170e+02  -0.023  0.98152    
stenose50-99%             -2.812e+01  1.077e+03  -0.026  0.97917    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 580.93  on 524  degrees of freedom
Residual deviance: 521.97  on 506  degrees of freedom
AIC: 559.97

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.019116 
Standard error............: 0.006065 
Odds ratio (effect size)..: 1.019 
Lower 95% CI..............: 1.007 
Upper 95% CI..............: 1.031 
Z-value...................: 3.151719 
P-value...................: 0.001623123 
Hosmer and Lemeshow r^2...: 0.101483 
Cox and Snell r^2.........: 0.106219 
Nagelkerke's pseudo r^2...: 0.158703 
Sample size of AE DB......: 611 
Sample size of model......: 525 
Missing data %............: 14.07529 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_epoch, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)    Gendermale  ORdate_epoch  
   8.149e+01     4.364e-01    -6.074e-09  

Degrees of Freedom: 523 Total (i.e. Null);  521 Residual
Null Deviance:      697.8 
Residual Deviance: 662.8    AIC: 668.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0537  -1.1708   0.7148   0.9695   1.7294  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                9.619e+01  5.900e+02   0.163   0.8705    
currentDF[, TARGET]        7.191e-04  2.932e-03   0.245   0.8063    
Age                        5.330e-03  1.260e-02   0.423   0.6722    
Gendermale                 4.544e-01  2.259e-01   2.011   0.0443 *  
ORdate_epoch              -6.090e-09  1.191e-09  -5.116 3.12e-07 ***
Hypertension.compositeyes -2.525e-01  2.910e-01  -0.868   0.3856    
DiabetesStatusDiabetes    -1.713e-01  2.357e-01  -0.727   0.4674    
SmokerStatusEx-smoker      8.778e-02  2.178e-01   0.403   0.6869    
SmokerStatusNever smoked   2.782e-01  2.872e-01   0.969   0.3326    
Med.Statin.LLDyes         -2.047e-01  2.351e-01  -0.871   0.3839    
Med.all.antiplateletyes    2.090e-01  3.266e-01   0.640   0.5222    
GFR_MDRD                  -8.243e-05  1.573e-04  -0.524   0.6004    
BMI                        2.548e-05  3.279e-04   0.078   0.9381    
MedHx_CVDNo               -3.298e-01  1.991e-01  -1.656   0.0976 .  
stenose50-70%             -1.439e+01  5.898e+02  -0.024   0.9805    
stenose70-90%             -1.443e+01  5.898e+02  -0.024   0.9805    
stenose90-99%             -1.425e+01  5.898e+02  -0.024   0.9807    
stenose100% (Occlusion)   -1.512e+01  5.898e+02  -0.026   0.9795    
stenose50-99%             -2.838e+01  1.062e+03  -0.027   0.9787    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 697.75  on 523  degrees of freedom
Residual deviance: 651.46  on 505  degrees of freedom
AIC: 689.46

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: IPH 
Effect size...............: 0.000719 
Standard error............: 0.002932 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.995 
Upper 95% CI..............: 1.006 
Z-value...................: 0.245254 
P-value...................: 0.8062599 
Hosmer and Lemeshow r^2...: 0.066347 
Cox and Snell r^2.........: 0.084556 
Nagelkerke's pseudo r^2...: 0.114895 
Sample size of AE DB......: 611 
Sample size of model......: 524 
Missing data %............: 14.23895 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale          BMI  
  0.2421162    0.5078264   -0.0006331  

Degrees of Freedom: 521 Total (i.e. Null);  519 Residual
Null Deviance:      718 
Residual Deviance: 708  AIC: 714

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8509  -1.2174   0.8838   1.0686   1.4859  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.769e+01  1.498e+01   1.181   0.2378  
currentDF[, TARGET]        3.232e-03  3.085e-03   1.047   0.2949  
Age                       -2.216e-03  1.202e-02  -0.184   0.8537  
Gendermale                 5.568e-01  2.188e-01   2.544   0.0110 *
ORdate_epoch              -1.345e-09  1.115e-09  -1.206   0.2279  
Hypertension.compositeyes  8.827e-02  2.748e-01   0.321   0.7480  
DiabetesStatusDiabetes    -1.574e-01  2.281e-01  -0.690   0.4902  
SmokerStatusEx-smoker      9.348e-02  2.087e-01   0.448   0.6542  
SmokerStatusNever smoked   1.130e-01  2.731e-01   0.414   0.6791  
Med.Statin.LLDyes          2.816e-01  2.202e-01   1.279   0.2010  
Med.all.antiplateletyes    2.371e-02  3.137e-01   0.076   0.9397  
GFR_MDRD                  -1.221e-04  1.503e-04  -0.812   0.4167  
BMI                       -7.240e-04  3.146e-04  -2.301   0.0214 *
MedHx_CVDNo               -2.032e-02  1.916e-01  -0.106   0.9156  
stenose50-70%              1.050e-01  1.476e+00   0.071   0.9433  
stenose70-90%              5.171e-01  1.433e+00   0.361   0.7183  
stenose90-99%              3.313e-01  1.436e+00   0.231   0.8175  
stenose100% (Occlusion)   -1.394e-01  1.762e+00  -0.079   0.9370  
stenose50-99%             -1.298e+01  5.354e+02  -0.024   0.9807  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 718.05  on 521  degrees of freedom
Residual deviance: 697.63  on 503  degrees of freedom
AIC: 735.63

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.003232 
Standard error............: 0.003085 
Odds ratio (effect size)..: 1.003 
Lower 95% CI..............: 0.997 
Upper 95% CI..............: 1.009 
Z-value...................: 1.047467 
P-value...................: 0.2948845 
Hosmer and Lemeshow r^2...: 0.02844 
Cox and Snell r^2.........: 0.038366 
Nagelkerke's pseudo r^2...: 0.051339 
Sample size of AE DB......: 611 
Sample size of model......: 522 
Missing data %............: 14.56629 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + MedHx_CVD, family = binomial(link = "logit"), data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age          MedHx_CVDNo  
           1.393861            -0.005854            -0.019210             0.454945  

Degrees of Freedom: 522 Total (i.e. Null);  519 Residual
Null Deviance:      661.1 
Residual Deviance: 648  AIC: 656

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0579  -1.2956   0.7333   0.8952   1.8563  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.058e+01  1.622e+01  -0.652   0.5144  
currentDF[, TARGET]       -6.555e-03  2.996e-03  -2.188   0.0287 *
Age                       -2.122e-02  1.312e-02  -1.618   0.1057  
Gendermale                -3.581e-01  2.428e-01  -1.475   0.1402  
ORdate_epoch               8.644e-10  1.210e-09   0.714   0.4752  
Hypertension.compositeyes  1.427e-01  2.943e-01   0.485   0.6279  
DiabetesStatusDiabetes    -2.093e-01  2.396e-01  -0.874   0.3823  
SmokerStatusEx-smoker      6.681e-02  2.241e-01   0.298   0.7656  
SmokerStatusNever smoked  -4.101e-01  2.864e-01  -1.432   0.1521  
Med.Statin.LLDyes          5.011e-02  2.354e-01   0.213   0.8314  
Med.all.antiplateletyes   -1.768e-01  3.374e-01  -0.524   0.6002  
GFR_MDRD                  -4.990e-05  1.600e-04  -0.312   0.7551  
BMI                        2.333e-04  3.391e-04   0.688   0.4915  
MedHx_CVDNo                4.956e-01  2.104e-01   2.356   0.0185 *
stenose50-70%              2.082e-01  1.476e+00   0.141   0.8878  
stenose70-90%              5.682e-01  1.432e+00   0.397   0.6916  
stenose90-99%              1.037e+00  1.437e+00   0.722   0.4704  
stenose100% (Occlusion)   -1.405e-01  1.772e+00  -0.079   0.9368  
stenose50-99%              1.388e+01  5.354e+02   0.026   0.9793  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 661.08  on 522  degrees of freedom
Residual deviance: 632.23  on 504  degrees of freedom
AIC: 670.23

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.006555 
Standard error............: 0.002996 
Odds ratio (effect size)..: 0.993 
Lower 95% CI..............: 0.988 
Upper 95% CI..............: 0.999 
Z-value...................: -2.187728 
P-value...................: 0.02868944 
Hosmer and Lemeshow r^2...: 0.04363 
Cox and Snell r^2.........: 0.053656 
Nagelkerke's pseudo r^2...: 0.074783 
Sample size of AE DB......: 611 
Sample size of model......: 523 
Missing data %............: 14.40262 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque HDAC9 expression levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

  • stroke
  • TIA
  • retinal infarction
  • amaurosis fugax
  • asymptomatic

Model 1

In this model we correct for Age, Gender, and year of surgery.

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, family = binomial(link = "logit"))

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of HDAC9.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_epoch, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)    Gendermale  ORdate_epoch  
   3.550e+01     3.275e-01    -2.606e-09  

Degrees of Freedom: 610 Total (i.e. Null);  608 Residual
Null Deviance:      747.7 
Residual Deviance: 738.3    AIC: 744.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7976  -1.4056   0.7646   0.8692   1.2170  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)          3.516e+01  1.324e+01   2.656  0.00792 **
currentDF[, TARGET] -3.075e-03  2.614e-03  -1.177  0.23934   
Age                 -3.058e-03  9.998e-03  -0.306  0.75970   
Gendermale           3.271e-01  2.023e-01   1.617  0.10595   
ORdate_epoch        -2.565e-09  9.897e-10  -2.592  0.00954 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 747.66  on 610  degrees of freedom
Residual deviance: 736.86  on 606  degrees of freedom
AIC: 746.86

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.003075 
Standard error............: 0.002614 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.992 
Upper 95% CI..............: 1.002 
Z-value...................: -1.176627 
P-value...................: 0.2393443 
Hosmer and Lemeshow r^2...: 0.014449 
Cox and Snell r^2.........: 0.017525 
Nagelkerke's pseudo r^2...: 0.024829 
Sample size of AE DB......: 611 
Sample size of model......: 611 
Missing data %............: 0 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #              Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #              MedHx_CVD + stenose, 
    #            data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of HDAC9.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_epoch + 
    SmokerStatus + Med.Statin.LLD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                Gendermale              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes  
               4.297e+01                 4.081e-01                -3.214e-09                 3.289e-01                 7.996e-01                 3.428e-01  

Degrees of Freedom: 531 Total (i.e. Null);  526 Residual
Null Deviance:      665.1 
Residual Deviance: 644.5    AIC: 656.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0500  -1.2488   0.7118   0.8737   1.4555  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                4.441e+01  1.518e+01   2.926  0.00343 **
currentDF[, TARGET]       -2.286e-03  2.784e-03  -0.821  0.41168   
Age                       -1.548e-02  1.281e-02  -1.209  0.22680   
Gendermale                 4.700e-01  2.297e-01   2.046  0.04071 * 
ORdate_epoch              -3.352e-09  1.129e-09  -2.969  0.00298 **
Hypertension.compositeyes  2.935e-01  2.855e-01   1.028  0.30388   
DiabetesStatusDiabetes    -2.692e-01  2.396e-01  -1.123  0.26129   
SmokerStatusEx-smoker      3.799e-01  2.197e-01   1.729  0.08375 . 
SmokerStatusNever smoked   8.193e-01  3.076e-01   2.664  0.00773 **
Med.Statin.LLDyes          2.983e-01  2.323e-01   1.284  0.19918   
Med.all.antiplateletyes   -2.030e-01  3.460e-01  -0.587  0.55743   
GFR_MDRD                  -8.610e-05  1.612e-04  -0.534  0.59326   
BMI                       -1.234e-04  3.333e-04  -0.370  0.71128   
MedHx_CVDNo                2.352e-01  2.082e-01   1.130  0.25846   
stenose50-70%              8.132e-01  1.480e+00   0.550  0.58256   
stenose70-90%              1.334e+00  1.438e+00   0.927  0.35380   
stenose90-99%              8.834e-01  1.439e+00   0.614  0.53944   
stenose100% (Occlusion)   -6.692e-03  1.777e+00  -0.004  0.99700   
stenose50-99%              1.517e+01  5.354e+02   0.028  0.97740   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 665.11  on 531  degrees of freedom
Residual deviance: 630.10  on 513  degrees of freedom
AIC: 668.1

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AERNASE.clin.hdac9 ' the association of ' HDAC9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.hdac9 
Score/Exposure/biomarker..: HDAC9 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.002286 
Standard error............: 0.002784 
Odds ratio (effect size)..: 0.998 
Lower 95% CI..............: 0.992 
Upper 95% CI..............: 1.003 
Z-value...................: -0.820947 
P-value...................: 0.4116763 
Hosmer and Lemeshow r^2...: 0.052636 
Cox and Snell r^2.........: 0.063687 
Nagelkerke's pseudo r^2...: 0.089254 
Sample size of AE DB......: 611 
Sample size of model......: 532 
Missing data %............: 12.92962 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           rowNames = FALSE, colNmes = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque HDAC9 expression levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

  • incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
    • cerebral infarction (CVA/stroke)
    • cerebral bleeding (CVA/stroke)
    • fatal cerebral infarction
    • fatal cerebral bleeding.
  • incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
    • myocardial infarction (MI)
    • coronary angioplasty (PCI/PTCA)
    • cardiovascular death (exact cause to be investigated)
    • coronary bypass (CABG)
    • fatal myocardial infarction (MI)
    • sudden death.
  • cardiovascular death - variable: epcvdeath.3years, these include:
    • cardiovascular death (exact cause to be investigated)
    • fatal myocardial infarction (MI)
    • fatal cerebral infarction
    • fatal cerebral bleeding
    • sudden death
    • fatal heart failure
    • fatal aneurysm rupture
    • other cardiovascular death..

30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.

cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "max.followup", 
                                                        "FU.cutt.off.3years", "FU.cutt.off.30days", "FU.cutt.off.90days",
                                                        "ep_major_t_3years", "ep_stroke_t_3years", "ep_coronary_t_3years",
                                                        "ep_cvdeath_t_3years",
                                                        "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years"
                                                        )), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)


AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

  • MACE (epmajor.3years)
  • Stroke (epstroke.3years)
  • Coronary events (epcoronary.3years)
  • Cardiovascular death (epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"epmajor.30days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epstroke.30days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcoronary.30days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.30days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epmajor.90days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epstroke.90days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcoronary.90days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.90days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AERNASE.clin.hdac9)

AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
* Creating function to summarize Cox regression and prepare container for results.
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, target_of_interest){
  cat("Summarizing Cox regression results for '", target_of_interest ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(target_of_interest, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, target_of_interest, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", target_of_interest, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}
[1] "Printing the summary of: epmajor.3years"

  1   2 
529  79 
[1] "Printing the summary of: epstroke.3years"

  1   2 
561  47 
[1] "Printing the summary of: epcoronary.3years"

  1   2 
561  47 
[1] "Printing the summary of: epcvdeath.3years"

  1   2 
581  27 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.912   3.000   2.661   3.000   3.000       3 
[1] "Printing the summary of: ep_stroke_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.929   3.000   2.705   3.000   3.000       3 
[1] "Printing the summary of: ep_coronary_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.923   3.000   2.736   3.000   3.000       3 
[1] "Printing the summary of: ep_cvdeath_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0137  2.9500  3.0000  2.8238  3.0000  3.0000       3 
for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_stroke_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_coronary_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_cvdeath_t_3years"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}
[1] "Printing the summary of: epmajor.30days"

  0   2 
529  79 
[1] "Printing the summary of: epstroke.30days"

  0   2 
561  47 
[1] "Printing the summary of: epcoronary.30days"

  0   1   2 
559   2  47 
[1] "Printing the summary of: epcvdeath.30days"

  0   2 
581  27 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.03   30.00   30.00       3 
[1] "Printing the summary of: ep_stroke_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.17   30.00   30.00       3 
[1] "Printing the summary of: ep_coronary_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.67   30.00   30.00       3 
[1] "Printing the summary of: ep_cvdeath_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  5.003  30.000  30.000  29.936  30.000  30.000       3 
for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_stroke_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_coronary_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_cvdeath_t_30days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}
[1] "Printing the summary of: epmajor.90days"

  0   2 
529  79 
[1] "Printing the summary of: epstroke.90days"

  0   1   2 
560   1  47 
[1] "Printing the summary of: epcoronary.90days"

  0   1   2 
559   2  47 
[1] "Printing the summary of: epcvdeath.90days"

  0   2 
581  27 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   86.73   90.00   90.00       3 
[1] "Printing the summary of: ep_stroke_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   87.19   90.00   90.00       3 
[1] "Printing the summary of: ep_coronary_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   88.73   90.00   90.00       3 
[1] "Printing the summary of: ep_cvdeath_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  5.003  90.000  90.000  89.668  90.000  90.000       3 
for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_stroke_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_coronary_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Warning: Removed 3 rows containing non-finite values (stat_bin).
Saving 18 x 12 in image
Warning: Removed 3 rows containing non-finite values (stat_bin).

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

  • Model 1: adjusted for age, sex, and year of surgery
  • Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

3 years follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  14,15,16,17 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  13,14,15,16 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 608, number of events= 79 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.570e-01  6.998e-01  2.348e-01 -1.520   0.1285  
Age                                                        3.071e-02  1.031e+00  1.378e-02  2.229   0.0258 *
Gendermale                                                 7.655e-01  2.150e+00  3.277e-01  2.336   0.0195 *
ORdate_year2003                                           -1.131e+00  3.228e-01  5.869e-01 -1.927   0.0540 .
ORdate_year2004                                           -7.715e-01  4.623e-01  5.189e-01 -1.487   0.1371  
ORdate_year2005                                           -4.270e-01  6.524e-01  4.701e-01 -0.908   0.3637  
ORdate_year2006                                           -3.803e-01  6.836e-01  4.692e-01 -0.811   0.4176  
ORdate_year2007                                           -1.480e+00  2.275e-01  6.281e-01 -2.357   0.0184 *
ORdate_year2008                                           -2.539e-01  7.758e-01  4.763e-01 -0.533   0.5940  
ORdate_year2009                                           -9.296e-01  3.947e-01  5.392e-01 -1.724   0.0847 .
ORdate_year2010                                           -5.937e-01  5.523e-01  5.869e-01 -1.012   0.3117  
ORdate_year2011                                           -1.509e+00  2.211e-01  8.052e-01 -1.874   0.0609 .
ORdate_year2012                                           -8.457e-01  4.293e-01  6.949e-01 -1.217   0.2236  
ORdate_year2013                                           -1.715e+01  3.569e-08  3.792e+03 -0.005   0.9964  
ORdate_year2014                                           -1.681e+01  4.994e-08  4.843e+03 -0.003   0.9972  
ORdate_year2015                                           -1.612e+01  9.973e-08  4.743e+03 -0.003   0.9973  
ORdate_year2016                                           -1.630e+01  8.375e-08  8.557e+03 -0.002   0.9985  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.998e-01  1.429e+00   0.44164    1.1088
Age                                                       1.031e+00  9.698e-01   1.00372    1.0594
Gendermale                                                2.150e+00  4.651e-01   1.13108    4.0874
ORdate_year2003                                           3.228e-01  3.098e+00   0.10219    1.0197
ORdate_year2004                                           4.623e-01  2.163e+00   0.16720    1.2783
ORdate_year2005                                           6.524e-01  1.533e+00   0.25967    1.6394
ORdate_year2006                                           6.836e-01  1.463e+00   0.27255    1.7147
ORdate_year2007                                           2.275e-01  4.395e+00   0.06643    0.7793
ORdate_year2008                                           7.758e-01  1.289e+00   0.30498    1.9732
ORdate_year2009                                           3.947e-01  2.533e+00   0.13720    1.1357
ORdate_year2010                                           5.523e-01  1.811e+00   0.17480    1.7448
ORdate_year2011                                           2.211e-01  4.524e+00   0.04561    1.0713
ORdate_year2012                                           4.293e-01  2.330e+00   0.10997    1.6757
ORdate_year2013                                           3.569e-08  2.802e+07   0.00000       Inf
ORdate_year2014                                           4.994e-08  2.002e+07   0.00000       Inf
ORdate_year2015                                           9.973e-08  1.003e+07   0.00000       Inf
ORdate_year2016                                           8.375e-08  1.194e+07   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA

Concordance= 0.659  (se = 0.029 )
Likelihood ratio test= 29.34  on 17 df,   p=0.03
Wald test            = 23.89  on 17 df,   p=0.1
Score (logrank) test = 26.91  on 17 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epmajor.3years 
Protein...................: HDAC9 
Effect size...............: -0.35699 
Standard error............: 0.234834 
Odds ratio (effect size)..: 0.7 
Lower 95% CI..............: 0.442 
Upper 95% CI..............: 1.109 
T-value...................: -1.520178 
P-value...................: 0.1284662 
Sample size in model......: 608 
Number of events..........: 79 
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  14,15,16,17 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  13,14,15,16 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 608, number of events= 47 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.767e-01  6.862e-01  3.043e-01 -1.238   0.2157  
Age                                                        2.715e-02  1.028e+00  1.774e-02  1.530   0.1259  
Gendermale                                                 3.733e-01  1.453e+00  3.746e-01  0.997   0.3190  
ORdate_year2003                                           -9.921e-01  3.708e-01  7.657e-01 -1.296   0.1951  
ORdate_year2004                                           -8.059e-01  4.467e-01  7.090e-01 -1.137   0.2557  
ORdate_year2005                                           -3.037e-01  7.381e-01  6.135e-01 -0.495   0.6206  
ORdate_year2006                                           -7.952e-03  9.921e-01  5.921e-01 -0.013   0.9893  
ORdate_year2007                                           -1.522e+00  2.182e-01  8.675e-01 -1.755   0.0793 .
ORdate_year2008                                           -1.811e-01  8.344e-01  6.274e-01 -0.289   0.7729  
ORdate_year2009                                           -1.517e+00  2.194e-01  8.714e-01 -1.741   0.0817 .
ORdate_year2010                                           -1.578e-01  8.540e-01  7.092e-01 -0.222   0.8239  
ORdate_year2011                                           -1.554e+00  2.113e-01  1.122e+00 -1.385   0.1660  
ORdate_year2012                                           -5.545e-01  5.744e-01  8.714e-01 -0.636   0.5246  
ORdate_year2013                                           -1.699e+01  4.164e-08  5.061e+03 -0.003   0.9973  
ORdate_year2014                                           -1.666e+01  5.831e-08  6.455e+03 -0.003   0.9979  
ORdate_year2015                                           -1.622e+01  9.003e-08  6.501e+03 -0.002   0.9980  
ORdate_year2016                                           -1.654e+01  6.526e-08  1.140e+04 -0.001   0.9988  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.862e-01  1.457e+00   0.37795     1.246
Age                                                       1.028e+00  9.732e-01   0.99241     1.064
Gendermale                                                1.453e+00  6.884e-01   0.69706     3.027
ORdate_year2003                                           3.708e-01  2.697e+00   0.08268     1.663
ORdate_year2004                                           4.467e-01  2.239e+00   0.11131     1.793
ORdate_year2005                                           7.381e-01  1.355e+00   0.22175     2.457
ORdate_year2006                                           9.921e-01  1.008e+00   0.31087     3.166
ORdate_year2007                                           2.182e-01  4.582e+00   0.03985     1.195
ORdate_year2008                                           8.344e-01  1.198e+00   0.24397     2.854
ORdate_year2009                                           2.194e-01  4.558e+00   0.03976     1.210
ORdate_year2010                                           8.540e-01  1.171e+00   0.21271     3.429
ORdate_year2011                                           2.113e-01  4.732e+00   0.02344     1.906
ORdate_year2012                                           5.744e-01  1.741e+00   0.10409     3.169
ORdate_year2013                                           4.164e-08  2.402e+07   0.00000       Inf
ORdate_year2014                                           5.831e-08  1.715e+07   0.00000       Inf
ORdate_year2015                                           9.003e-08  1.111e+07   0.00000       Inf
ORdate_year2016                                           6.526e-08  1.532e+07   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA

Concordance= 0.678  (se = 0.035 )
Likelihood ratio test= 19.58  on 17 df,   p=0.3
Wald test            = 15.36  on 17 df,   p=0.6
Score (logrank) test = 18.04  on 17 df,   p=0.4


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epstroke.3years 
Protein...................: HDAC9 
Effect size...............: -0.376659 
Standard error............: 0.304261 
Odds ratio (effect size)..: 0.686 
Lower 95% CI..............: 0.378 
Upper 95% CI..............: 1.246 
T-value...................: -1.237946 
P-value...................: 0.215736 
Sample size in model......: 608 
Number of events..........: 47 
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  14,15,16,17 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  13,14,15,16 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 608, number of events= 47 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -3.436e-01  7.092e-01  3.018e-01 -1.138   0.2549  
Age                                                        5.263e-03  1.005e+00  1.746e-02  0.301   0.7631  
Gendermale                                                 1.087e+00  2.966e+00  4.758e-01  2.285   0.0223 *
ORdate_year2003                                           -6.180e-01  5.390e-01  6.069e-01 -1.018   0.3086  
ORdate_year2004                                           -1.436e+00  2.379e-01  7.325e-01 -1.960   0.0500 *
ORdate_year2005                                           -6.738e-01  5.097e-01  5.879e-01 -1.146   0.2517  
ORdate_year2006                                           -3.341e-01  7.160e-01  5.482e-01 -0.609   0.5423  
ORdate_year2007                                           -1.082e+00  3.389e-01  6.726e-01 -1.609   0.1076  
ORdate_year2008                                           -7.672e-01  4.643e-01  6.336e-01 -1.211   0.2260  
ORdate_year2009                                           -1.078e+00  3.401e-01  6.741e-01 -1.600   0.1096  
ORdate_year2010                                           -1.868e+00  1.545e-01  1.096e+00 -1.704   0.0884 .
ORdate_year2011                                           -1.777e+00  1.691e-01  1.098e+00 -1.619   0.1055  
ORdate_year2012                                           -1.586e+00  2.047e-01  1.100e+00 -1.442   0.1494  
ORdate_year2013                                           -1.739e+01  2.809e-08  5.091e+03 -0.003   0.9973  
ORdate_year2014                                           -1.714e+01  3.587e-08  6.484e+03 -0.003   0.9979  
ORdate_year2015                                           -1.643e+01  7.331e-08  6.318e+03 -0.003   0.9979  
ORdate_year2016                                           -1.629e+01  8.444e-08  1.132e+04 -0.001   0.9989  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 7.092e-01  1.410e+00   0.39254    1.2814
Age                                                       1.005e+00  9.948e-01   0.97146    1.0403
Gendermale                                                2.966e+00  3.371e-01   1.16735    7.5375
ORdate_year2003                                           5.390e-01  1.855e+00   0.16405    1.7710
ORdate_year2004                                           2.379e-01  4.204e+00   0.05661    0.9997
ORdate_year2005                                           5.097e-01  1.962e+00   0.16104    1.6135
ORdate_year2006                                           7.160e-01  1.397e+00   0.24450    2.0967
ORdate_year2007                                           3.389e-01  2.951e+00   0.09069    1.2663
ORdate_year2008                                           4.643e-01  2.154e+00   0.13413    1.6075
ORdate_year2009                                           3.401e-01  2.940e+00   0.09076    1.2748
ORdate_year2010                                           1.545e-01  6.474e+00   0.01802    1.3241
ORdate_year2011                                           1.691e-01  5.914e+00   0.01966    1.4544
ORdate_year2012                                           2.047e-01  4.884e+00   0.02371    1.7684
ORdate_year2013                                           2.809e-08  3.560e+07   0.00000       Inf
ORdate_year2014                                           3.587e-08  2.787e+07   0.00000       Inf
ORdate_year2015                                           7.331e-08  1.364e+07   0.00000       Inf
ORdate_year2016                                           8.444e-08  1.184e+07   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA

Concordance= 0.668  (se = 0.037 )
Likelihood ratio test= 19.7  on 17 df,   p=0.3
Wald test            = 15.53  on 17 df,   p=0.6
Score (logrank) test = 18.03  on 17 df,   p=0.4


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcoronary.3years 
Protein...................: HDAC9 
Effect size...............: -0.343584 
Standard error............: 0.301808 
Odds ratio (effect size)..: 0.709 
Lower 95% CI..............: 0.393 
Upper 95% CI..............: 1.281 
T-value...................: -1.138421 
P-value...................: 0.2549448 
Sample size in model......: 608 
Number of events..........: 47 
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  12,13,14,15,16,17 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  11,12,13,14,15,16 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 608, number of events= 27 
   (3 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -8.607e-01  4.229e-01  4.346e-01 -1.980  0.04768 * 
Age                                                        7.113e-02  1.074e+00  2.628e-02  2.707  0.00679 **
Gendermale                                                 6.115e-01  1.843e+00  5.456e-01  1.121  0.26237   
ORdate_year2003                                           -1.596e+00  2.027e-01  8.395e-01 -1.901  0.05732 . 
ORdate_year2004                                           -1.704e+00  1.819e-01  8.376e-01 -2.035  0.04187 * 
ORdate_year2005                                           -9.918e-01  3.709e-01  6.332e-01 -1.566  0.11730   
ORdate_year2006                                           -1.827e+00  1.609e-01  8.390e-01 -2.178  0.02942 * 
ORdate_year2007                                           -1.713e+00  1.804e-01  8.386e-01 -2.042  0.04111 * 
ORdate_year2008                                           -1.245e+00  2.878e-01  7.311e-01 -1.703  0.08850 . 
ORdate_year2009                                           -9.372e-01  3.917e-01  6.856e-01 -1.367  0.17163   
ORdate_year2010                                           -1.059e+00  3.469e-01  8.396e-01 -1.261  0.20731   
ORdate_year2011                                           -1.917e+01  4.712e-09  5.489e+03 -0.003  0.99721   
ORdate_year2012                                           -1.930e+01  4.137e-09  6.718e+03 -0.003  0.99771   
ORdate_year2013                                           -1.960e+01  3.087e-09  1.480e+04 -0.001  0.99894   
ORdate_year2014                                           -1.877e+01  7.060e-09  1.853e+04 -0.001  0.99919   
ORdate_year2015                                           -1.790e+01  1.677e-08  1.934e+04 -0.001  0.99926   
ORdate_year2016                                           -1.875e+01  7.211e-09  3.554e+04 -0.001  0.99958   
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 4.229e-01  2.365e+00   0.18041    0.9913
Age                                                       1.074e+00  9.313e-01   1.01982    1.1305
Gendermale                                                1.843e+00  5.425e-01   0.63265    5.3700
ORdate_year2003                                           2.027e-01  4.932e+00   0.03912    1.0509
ORdate_year2004                                           1.819e-01  5.497e+00   0.03523    0.9393
ORdate_year2005                                           3.709e-01  2.696e+00   0.10722    1.2832
ORdate_year2006                                           1.609e-01  6.216e+00   0.03107    0.8329
ORdate_year2007                                           1.804e-01  5.544e+00   0.03486    0.9332
ORdate_year2008                                           2.878e-01  3.474e+00   0.06867    1.2063
ORdate_year2009                                           3.917e-01  2.553e+00   0.10219    1.5016
ORdate_year2010                                           3.469e-01  2.883e+00   0.06691    1.7984
ORdate_year2011                                           4.712e-09  2.122e+08   0.00000       Inf
ORdate_year2012                                           4.137e-09  2.417e+08   0.00000       Inf
ORdate_year2013                                           3.087e-09  3.239e+08   0.00000       Inf
ORdate_year2014                                           7.060e-09  1.417e+08   0.00000       Inf
ORdate_year2015                                           1.677e-08  5.964e+07   0.00000       Inf
ORdate_year2016                                           7.211e-09  1.387e+08   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA

Concordance= 0.792  (se = 0.037 )
Likelihood ratio test= 29.29  on 17 df,   p=0.03
Wald test            = 15.29  on 17 df,   p=0.6
Score (logrank) test = 29.48  on 17 df,   p=0.03


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: HDAC9 
Effect size...............: -0.860664 
Standard error............: 0.434641 
Odds ratio (effect size)..: 0.423 
Lower 95% CI..............: 0.18 
Upper 95% CI..............: 0.991 
T-value...................: -1.980172 
P-value...................: 0.04768416 
Sample size in model......: 608 
Number of events..........: 27 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  14,15,16,17,33,34,35 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  13,14,15,16,32,33,34 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 530, number of events= 67 
   (81 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.318e-01  8.765e-01  2.604e-01 -0.506  0.61280   
Age                                                        4.473e-02  1.046e+00  1.822e-02  2.454  0.01411 * 
Gendermale                                                 7.081e-01  2.030e+00  3.675e-01  1.927  0.05401 . 
ORdate_year2003                                           -1.039e+00  3.540e-01  6.559e-01 -1.583  0.11334   
ORdate_year2004                                           -5.447e-01  5.800e-01  5.701e-01 -0.955  0.33937   
ORdate_year2005                                           -6.583e-02  9.363e-01  5.329e-01 -0.124  0.90168   
ORdate_year2006                                            6.261e-03  1.006e+00  5.220e-01  0.012  0.99043   
ORdate_year2007                                           -1.106e+00  3.309e-01  6.744e-01 -1.640  0.10107   
ORdate_year2008                                            7.153e-02  1.074e+00  5.518e-01  0.130  0.89686   
ORdate_year2009                                           -9.449e-01  3.887e-01  6.423e-01 -1.471  0.14124   
ORdate_year2010                                           -2.599e-01  7.711e-01  6.334e-01 -0.410  0.68158   
ORdate_year2011                                           -1.020e+00  3.605e-01  8.449e-01 -1.208  0.22715   
ORdate_year2012                                           -2.814e-01  7.548e-01  8.582e-01 -0.328  0.74302   
ORdate_year2013                                           -1.658e+01  6.296e-08  4.559e+03 -0.004  0.99710   
ORdate_year2014                                           -1.612e+01  9.973e-08  1.005e+04 -0.002  0.99872   
ORdate_year2015                                           -1.616e+01  9.562e-08  4.869e+03 -0.003  0.99735   
ORdate_year2016                                           -1.527e+01  2.330e-07  1.005e+04 -0.002  0.99879   
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA   
Hypertension.compositeyes                                  7.024e-01  2.019e+00  5.423e-01  1.295  0.19529   
DiabetesStatusDiabetes                                     7.616e-01  2.142e+00  2.689e-01  2.832  0.00463 **
SmokerStatusEx-smoker                                     -5.310e-01  5.880e-01  2.720e-01 -1.952  0.05093 . 
SmokerStatusNever smoked                                  -8.937e-01  4.091e-01  4.383e-01 -2.039  0.04143 * 
Med.Statin.LLDyes                                         -2.653e-01  7.670e-01  2.906e-01 -0.913  0.36141   
Med.all.antiplateletyes                                   -2.405e-01  7.863e-01  4.075e-01 -0.590  0.55513   
GFR_MDRD                                                  -1.692e-04  9.998e-01  1.982e-04 -0.853  0.39341   
BMI                                                        7.750e-04  1.001e+00  4.414e-04  1.756  0.07916 . 
MedHx_CVDNo                                               -6.775e-01  5.079e-01  3.037e-01 -2.231  0.02569 * 
stenose50-70%                                              1.549e+01  5.346e+06  5.017e+03  0.003  0.99754   
stenose70-90%                                              1.611e+01  9.964e+06  5.017e+03  0.003  0.99744   
stenose90-99%                                              1.618e+01  1.060e+07  5.017e+03  0.003  0.99743   
stenose100% (Occlusion)                                    5.304e-01  1.700e+00  6.605e+03  0.000  0.99994   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -6.599e-01  5.169e-01  1.123e+04  0.000  0.99995   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.765e-01  1.141e+00   0.52619    1.4602
Age                                                       1.046e+00  9.563e-01   1.00905    1.0838
Gendermale                                                2.030e+00  4.926e-01   0.98786    4.1723
ORdate_year2003                                           3.540e-01  2.825e+00   0.09786    1.2802
ORdate_year2004                                           5.800e-01  1.724e+00   0.18974    1.7730
ORdate_year2005                                           9.363e-01  1.068e+00   0.32949    2.6606
ORdate_year2006                                           1.006e+00  9.938e-01   0.36174    2.7992
ORdate_year2007                                           3.309e-01  3.022e+00   0.08823    1.2411
ORdate_year2008                                           1.074e+00  9.310e-01   0.36421    3.1679
ORdate_year2009                                           3.887e-01  2.573e+00   0.11039    1.3688
ORdate_year2010                                           7.711e-01  1.297e+00   0.22282    2.6687
ORdate_year2011                                           3.605e-01  2.774e+00   0.06881    1.8880
ORdate_year2012                                           7.548e-01  1.325e+00   0.14038    4.0579
ORdate_year2013                                           6.296e-08  1.588e+07   0.00000       Inf
ORdate_year2014                                           9.973e-08  1.003e+07   0.00000       Inf
ORdate_year2015                                           9.562e-08  1.046e+07   0.00000       Inf
ORdate_year2016                                           2.329e-07  4.293e+06   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA
Hypertension.compositeyes                                 2.019e+00  4.954e-01   0.69727    5.8437
DiabetesStatusDiabetes                                    2.142e+00  4.669e-01   1.26426    3.6279
SmokerStatusEx-smoker                                     5.880e-01  1.701e+00   0.34503    1.0022
SmokerStatusNever smoked                                  4.091e-01  2.444e+00   0.17331    0.9659
Med.Statin.LLDyes                                         7.670e-01  1.304e+00   0.43391    1.3558
Med.all.antiplateletyes                                   7.863e-01  1.272e+00   0.35377    1.7475
GFR_MDRD                                                  9.998e-01  1.000e+00   0.99944    1.0002
BMI                                                       1.001e+00  9.992e-01   0.99991    1.0016
MedHx_CVDNo                                               5.079e-01  1.969e+00   0.28006    0.9210
stenose50-70%                                             5.346e+06  1.871e-07   0.00000       Inf
stenose70-90%                                             9.964e+06  1.004e-07   0.00000       Inf
stenose90-99%                                             1.060e+07  9.435e-08   0.00000       Inf
stenose100% (Occlusion)                                   1.700e+00  5.883e-01   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             5.169e-01  1.935e+00   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.763  (se = 0.026 )
Likelihood ratio test= 59  on 31 df,   p=0.002
Wald test            = 44.52  on 31 df,   p=0.05
Score (logrank) test = 57.21  on 31 df,   p=0.003


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epmajor.3years 
Protein...................: HDAC9 
Effect size...............: -0.131769 
Standard error............: 0.260373 
Odds ratio (effect size)..: 0.877 
Lower 95% CI..............: 0.526 
Upper 95% CI..............: 1.46 
T-value...................: -0.506077 
P-value...................: 0.6128024 
Sample size in model......: 530 
Number of events..........: 67 
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  14,15,16,17,33,34,35 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  13,14,15,16,32,33,34 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 530, number of events= 37 
   (81 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.703e-01  8.434e-01  3.510e-01 -0.485   0.6276  
Age                                                        5.147e-02  1.053e+00  2.370e-02  2.172   0.0298 *
Gendermale                                                 2.527e-01  1.287e+00  4.348e-01  0.581   0.5612  
ORdate_year2003                                           -1.044e+00  3.522e-01  9.289e-01 -1.124   0.2612  
ORdate_year2004                                           -6.143e-01  5.410e-01  8.312e-01 -0.739   0.4599  
ORdate_year2005                                           -1.172e-01  8.894e-01  7.372e-01 -0.159   0.8736  
ORdate_year2006                                            4.584e-01  1.582e+00  6.917e-01  0.663   0.5075  
ORdate_year2007                                           -1.053e+00  3.487e-01  9.485e-01 -1.111   0.2667  
ORdate_year2008                                            7.231e-02  1.075e+00  7.590e-01  0.095   0.9241  
ORdate_year2009                                           -1.800e+00  1.654e-01  1.192e+00 -1.510   0.1311  
ORdate_year2010                                            1.386e-01  1.149e+00  8.076e-01  0.172   0.8638  
ORdate_year2011                                           -9.355e-01  3.924e-01  1.181e+00 -0.792   0.4283  
ORdate_year2012                                           -1.095e-01  8.963e-01  1.209e+00 -0.091   0.9278  
ORdate_year2013                                           -1.640e+01  7.545e-08  6.617e+03 -0.002   0.9980  
ORdate_year2014                                           -1.597e+01  1.158e-07  1.419e+04 -0.001   0.9991  
ORdate_year2015                                           -1.627e+01  8.581e-08  7.550e+03 -0.002   0.9983  
ORdate_year2016                                           -1.526e+01  2.347e-07  1.419e+04 -0.001   0.9991  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA  
Hypertension.compositeyes                                  3.872e-01  1.473e+00  6.468e-01  0.599   0.5494  
DiabetesStatusDiabetes                                     5.080e-01  1.662e+00  3.825e-01  1.328   0.1842  
SmokerStatusEx-smoker                                     -5.848e-01  5.572e-01  3.580e-01 -1.634   0.1023  
SmokerStatusNever smoked                                  -1.694e+00  1.837e-01  7.594e-01 -2.231   0.0257 *
Med.Statin.LLDyes                                         -2.400e-01  7.866e-01  3.863e-01 -0.621   0.5344  
Med.all.antiplateletyes                                   -1.004e-01  9.045e-01  5.734e-01 -0.175   0.8610  
GFR_MDRD                                                   2.910e-04  1.000e+00  2.682e-04  1.085   0.2779  
BMI                                                        1.079e-03  1.001e+00  5.996e-04  1.799   0.0721 .
MedHx_CVDNo                                               -5.917e-01  5.534e-01  3.977e-01 -1.488   0.1368  
stenose50-70%                                              1.563e+01  6.126e+06  6.652e+03  0.002   0.9981  
stenose70-90%                                              1.568e+01  6.437e+06  6.652e+03  0.002   0.9981  
stenose90-99%                                              1.574e+01  6.875e+06  6.652e+03  0.002   0.9981  
stenose100% (Occlusion)                                   -1.606e-01  8.516e-01  8.497e+03  0.000   1.0000  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                             -1.751e+00  1.737e-01  1.567e+04  0.000   0.9999  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.434e-01  1.186e+00   0.42391    1.6782
Age                                                       1.053e+00  9.498e-01   1.00505    1.1029
Gendermale                                                1.287e+00  7.767e-01   0.54902    3.0190
ORdate_year2003                                           3.522e-01  2.840e+00   0.05703    2.1747
ORdate_year2004                                           5.410e-01  1.848e+00   0.10609    2.7591
ORdate_year2005                                           8.894e-01  1.124e+00   0.20968    3.7722
ORdate_year2006                                           1.582e+00  6.323e-01   0.40766    6.1356
ORdate_year2007                                           3.487e-01  2.868e+00   0.05434    2.2380
ORdate_year2008                                           1.075e+00  9.302e-01   0.24287    4.7580
ORdate_year2009                                           1.654e-01  6.047e+00   0.01599    1.7100
ORdate_year2010                                           1.149e+00  8.706e-01   0.23590    5.5927
ORdate_year2011                                           3.924e-01  2.548e+00   0.03876    3.9720
ORdate_year2012                                           8.963e-01  1.116e+00   0.08386    9.5788
ORdate_year2013                                           7.545e-08  1.325e+07   0.00000       Inf
ORdate_year2014                                           1.158e-07  8.636e+06   0.00000       Inf
ORdate_year2015                                           8.581e-08  1.165e+07   0.00000       Inf
ORdate_year2016                                           2.347e-07  4.261e+06   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA
Hypertension.compositeyes                                 1.473e+00  6.789e-01   0.41460    5.2326
DiabetesStatusDiabetes                                    1.662e+00  6.017e-01   0.78520    3.5174
SmokerStatusEx-smoker                                     5.572e-01  1.795e+00   0.27625    1.1239
SmokerStatusNever smoked                                  1.837e-01  5.443e+00   0.04147    0.8139
Med.Statin.LLDyes                                         7.866e-01  1.271e+00   0.36892    1.6772
Med.all.antiplateletyes                                   9.045e-01  1.106e+00   0.29399    2.7825
GFR_MDRD                                                  1.000e+00  9.997e-01   0.99977    1.0008
BMI                                                       1.001e+00  9.989e-01   0.99990    1.0023
MedHx_CVDNo                                               5.534e-01  1.807e+00   0.25382    1.2066
stenose50-70%                                             6.126e+06  1.633e-07   0.00000       Inf
stenose70-90%                                             6.437e+06  1.553e-07   0.00000       Inf
stenose90-99%                                             6.875e+06  1.455e-07   0.00000       Inf
stenose100% (Occlusion)                                   8.516e-01  1.174e+00   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             1.737e-01  5.759e+00   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.78  (se = 0.032 )
Likelihood ratio test= 37.58  on 31 df,   p=0.2
Wald test            = 26.88  on 31 df,   p=0.7
Score (logrank) test = 36.13  on 31 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epstroke.3years 
Protein...................: HDAC9 
Effect size...............: -0.170258 
Standard error............: 0.35101 
Odds ratio (effect size)..: 0.843 
Lower 95% CI..............: 0.424 
Upper 95% CI..............: 1.678 
T-value...................: -0.485054 
P-value...................: 0.6276384 
Sample size in model......: 530 
Number of events..........: 37 
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  14,15,16,17,34,35 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  13,14,15,16,33,34 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 530, number of events= 42 
   (81 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -1.917e-01  8.256e-01  3.275e-01 -0.585  0.55831   
Age                                                        4.240e-03  1.004e+00  2.322e-02  0.183  0.85513   
Gendermale                                                 1.516e+00  4.554e+00  6.104e-01  2.483  0.01301 * 
ORdate_year2003                                           -3.999e-01  6.704e-01  6.693e-01 -0.597  0.55018   
ORdate_year2004                                           -1.227e+00  2.933e-01  7.828e-01 -1.567  0.11711   
ORdate_year2005                                           -1.917e-01  8.256e-01  6.635e-01 -0.289  0.77266   
ORdate_year2006                                           -3.560e-01  7.005e-01  6.501e-01 -0.548  0.58394   
ORdate_year2007                                           -8.830e-01  4.135e-01  8.111e-01 -1.089  0.27628   
ORdate_year2008                                           -4.915e-01  6.117e-01  7.423e-01 -0.662  0.50790   
ORdate_year2009                                           -8.584e-01  4.238e-01  7.545e-01 -1.138  0.25525   
ORdate_year2010                                           -1.852e+00  1.569e-01  1.148e+00 -1.613  0.10664   
ORdate_year2011                                           -1.669e+00  1.885e-01  1.162e+00 -1.436  0.15109   
ORdate_year2012                                           -9.168e-01  3.998e-01  1.167e+00 -0.786  0.43213   
ORdate_year2013                                           -1.926e+01  4.303e-09  1.620e+04 -0.001  0.99905   
ORdate_year2014                                           -1.794e+01  1.620e-08  3.501e+04 -0.001  0.99959   
ORdate_year2015                                           -1.742e+01  2.726e-08  1.583e+04 -0.001  0.99912   
ORdate_year2016                                           -1.742e+01  2.719e-08  3.501e+04  0.000  0.99960   
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA   
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA   
Hypertension.compositeyes                                  1.573e+00  4.821e+00  1.031e+00  1.526  0.12702   
DiabetesStatusDiabetes                                     3.327e-01  1.395e+00  3.492e-01  0.953  0.34068   
SmokerStatusEx-smoker                                     -2.464e-01  7.816e-01  3.558e-01 -0.693  0.48857   
SmokerStatusNever smoked                                   1.276e-01  1.136e+00  4.770e-01  0.267  0.78908   
Med.Statin.LLDyes                                          4.294e-01  1.536e+00  4.406e-01  0.974  0.32983   
Med.all.antiplateletyes                                    2.228e-02  1.023e+00  5.714e-01  0.039  0.96890   
GFR_MDRD                                                  -4.257e-04  9.996e-01  2.562e-04 -1.662  0.09661 . 
BMI                                                        4.625e-04  1.000e+00  5.521e-04  0.838  0.40220   
MedHx_CVDNo                                               -1.129e+00  3.234e-01  4.271e-01 -2.643  0.00822 **
stenose50-70%                                              1.097e+00  2.994e+00  1.877e+04  0.000  0.99995   
stenose70-90%                                              1.896e+01  1.717e+08  1.797e+04  0.001  0.99916   
stenose90-99%                                              1.884e+01  1.519e+08  1.797e+04  0.001  0.99916   
stenose100% (Occlusion)                                    1.314e+00  3.722e+00  2.298e+04  0.000  0.99995   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                              1.578e+00  4.847e+00  3.935e+04  0.000  0.99997   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 8.256e-01  1.211e+00   0.43451     1.569
Age                                                       1.004e+00  9.958e-01   0.95957     1.051
Gendermale                                                4.554e+00  2.196e-01   1.37644    15.064
ORdate_year2003                                           6.704e-01  1.492e+00   0.18057     2.489
ORdate_year2004                                           2.933e-01  3.410e+00   0.06324     1.360
ORdate_year2005                                           8.256e-01  1.211e+00   0.22491     3.030
ORdate_year2006                                           7.005e-01  1.428e+00   0.19591     2.505
ORdate_year2007                                           4.135e-01  2.418e+00   0.08435     2.027
ORdate_year2008                                           6.117e-01  1.635e+00   0.14279     2.621
ORdate_year2009                                           4.238e-01  2.359e+00   0.09659     1.860
ORdate_year2010                                           1.569e-01  6.373e+00   0.01654     1.488
ORdate_year2011                                           1.885e-01  5.306e+00   0.01931     1.839
ORdate_year2012                                           3.998e-01  2.501e+00   0.04060     3.938
ORdate_year2013                                           4.303e-09  2.324e+08   0.00000       Inf
ORdate_year2014                                           1.620e-08  6.172e+07   0.00000       Inf
ORdate_year2015                                           2.726e-08  3.668e+07   0.00000       Inf
ORdate_year2016                                           2.719e-08  3.678e+07   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA
Hypertension.compositeyes                                 4.821e+00  2.074e-01   0.63929    36.356
DiabetesStatusDiabetes                                    1.395e+00  7.170e-01   0.70348     2.765
SmokerStatusEx-smoker                                     7.816e-01  1.279e+00   0.38919     1.570
SmokerStatusNever smoked                                  1.136e+00  8.802e-01   0.44605     2.894
Med.Statin.LLDyes                                         1.536e+00  6.509e-01   0.64775     3.644
Med.all.antiplateletyes                                   1.023e+00  9.780e-01   0.33366     3.134
GFR_MDRD                                                  9.996e-01  1.000e+00   0.99907     1.000
BMI                                                       1.000e+00  9.995e-01   0.99938     1.002
MedHx_CVDNo                                               3.234e-01  3.092e+00   0.14003     0.747
stenose50-70%                                             2.994e+00  3.340e-01   0.00000       Inf
stenose70-90%                                             1.717e+08  5.826e-09   0.00000       Inf
stenose90-99%                                             1.519e+08  6.583e-09   0.00000       Inf
stenose100% (Occlusion)                                   3.722e+00  2.687e-01   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             4.847e+00  2.063e-01   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.784  (se = 0.026 )
Likelihood ratio test= 46.96  on 31 df,   p=0.03
Wald test            = 24.64  on 31 df,   p=0.8
Score (logrank) test = 38.53  on 31 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcoronary.3years 
Protein...................: HDAC9 
Effect size...............: -0.191689 
Standard error............: 0.327479 
Odds ratio (effect size)..: 0.826 
Lower 95% CI..............: 0.435 
Upper 95% CI..............: 1.569 
T-value...................: -0.585348 
P-value...................: 0.5583135 
Sample size in model......: 530 
Number of events..........: 42 
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [HDAC9]; 1 out of 1 target-of-interest.
   > cross tabulation of HDAC9-stratum.

[ 0, 22) [22,449] 
     314      297 

   > fitting the model for HDAC9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  12,13,14,15,16,17,24,33,34,35,38 ; coefficient may be infinite. 
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  11,12,13,14,15,16,23,32,33,34,37 ; coefficient may be infinite. 

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 530, number of events= 23 
   (81 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] -4.178e-01  6.585e-01  4.762e-01 -0.877   0.3803  
Age                                                        7.242e-02  1.075e+00  3.502e-02  2.068   0.0387 *
Gendermale                                                 3.605e-01  1.434e+00  5.808e-01  0.621   0.5348  
ORdate_year2003                                           -1.450e+00  2.346e-01  9.303e-01 -1.559   0.1191  
ORdate_year2004                                           -1.161e+00  3.132e-01  9.034e-01 -1.285   0.1987  
ORdate_year2005                                           -4.916e-01  6.117e-01  7.785e-01 -0.631   0.5278  
ORdate_year2006                                           -1.264e+00  2.825e-01  9.076e-01 -1.393   0.1637  
ORdate_year2007                                           -1.552e+00  2.118e-01  9.501e-01 -1.634   0.1023  
ORdate_year2008                                           -5.706e-01  5.652e-01  8.332e-01 -0.685   0.4935  
ORdate_year2009                                           -1.415e+00  2.430e-01  9.732e-01 -1.454   0.1460  
ORdate_year2010                                           -5.791e-01  5.604e-01  9.477e-01 -0.611   0.5412  
ORdate_year2011                                           -2.076e+01  9.612e-10  1.356e+04 -0.002   0.9988  
ORdate_year2012                                           -2.036e+01  1.438e-09  2.044e+04 -0.001   0.9992  
ORdate_year2013                                           -2.053e+01  1.209e-09  4.964e+04  0.000   0.9997  
ORdate_year2014                                           -1.974e+01  2.686e-09  1.173e+05  0.000   0.9999  
ORdate_year2015                                           -2.054e+01  1.202e-09  4.934e+04  0.000   0.9997  
ORdate_year2016                                           -1.913e+01  4.896e-09  1.173e+05  0.000   0.9999  
ORdate_year2017                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2018                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2019                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2020                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2021                                                   NA         NA  0.000e+00     NA       NA  
ORdate_year2022                                                   NA         NA  0.000e+00     NA       NA  
Hypertension.compositeyes                                  1.907e+01  1.918e+08  7.276e+03  0.003   0.9979  
DiabetesStatusDiabetes                                     1.212e+00  3.361e+00  4.799e-01  2.526   0.0115 *
SmokerStatusEx-smoker                                     -8.080e-01  4.457e-01  4.866e-01 -1.661   0.0968 .
SmokerStatusNever smoked                                  -1.105e+00  3.313e-01  8.126e-01 -1.360   0.1740  
Med.Statin.LLDyes                                         -5.452e-01  5.797e-01  4.731e-01 -1.152   0.2492  
Med.all.antiplateletyes                                   -1.117e+00  3.272e-01  5.923e-01 -1.886   0.0593 .
GFR_MDRD                                                  -6.217e-04  9.994e-01  3.621e-04 -1.717   0.0860 .
BMI                                                        8.061e-04  1.001e+00  8.100e-04  0.995   0.3197  
MedHx_CVDNo                                               -7.928e-01  4.526e-01  5.899e-01 -1.344   0.1790  
stenose50-70%                                              1.913e+01  2.025e+08  3.916e+04  0.000   0.9996  
stenose70-90%                                              1.905e+01  1.880e+08  3.916e+04  0.000   0.9996  
stenose90-99%                                              1.914e+01  2.049e+08  3.916e+04  0.000   0.9996  
stenose100% (Occlusion)                                    2.515e+00  1.237e+01  4.692e+04  0.000   1.0000  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                              3.679e+01  9.479e+15  1.256e+05  0.000   0.9998  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][22,449] 6.585e-01  1.519e+00   0.25897     1.674
Age                                                       1.075e+00  9.301e-01   1.00379     1.151
Gendermale                                                1.434e+00  6.973e-01   0.45939     4.477
ORdate_year2003                                           2.346e-01  4.263e+00   0.03788     1.453
ORdate_year2004                                           3.132e-01  3.193e+00   0.05331     1.840
ORdate_year2005                                           6.117e-01  1.635e+00   0.13299     2.813
ORdate_year2006                                           2.825e-01  3.539e+00   0.04770     1.674
ORdate_year2007                                           2.118e-01  4.721e+00   0.03290     1.363
ORdate_year2008                                           5.652e-01  1.769e+00   0.11040     2.893
ORdate_year2009                                           2.430e-01  4.116e+00   0.03607     1.637
ORdate_year2010                                           5.604e-01  1.784e+00   0.08746     3.591
ORdate_year2011                                           9.612e-10  1.040e+09   0.00000       Inf
ORdate_year2012                                           1.438e-09  6.954e+08   0.00000       Inf
ORdate_year2013                                           1.209e-09  8.269e+08   0.00000       Inf
ORdate_year2014                                           2.686e-09  3.723e+08   0.00000       Inf
ORdate_year2015                                           1.202e-09  8.323e+08   0.00000       Inf
ORdate_year2016                                           4.896e-09  2.043e+08   0.00000       Inf
ORdate_year2017                                                  NA         NA        NA        NA
ORdate_year2018                                                  NA         NA        NA        NA
ORdate_year2019                                                  NA         NA        NA        NA
ORdate_year2020                                                  NA         NA        NA        NA
ORdate_year2021                                                  NA         NA        NA        NA
ORdate_year2022                                                  NA         NA        NA        NA
Hypertension.compositeyes                                 1.918e+08  5.214e-09   0.00000       Inf
DiabetesStatusDiabetes                                    3.361e+00  2.975e-01   1.31207     8.609
SmokerStatusEx-smoker                                     4.457e-01  2.243e+00   0.17174     1.157
SmokerStatusNever smoked                                  3.313e-01  3.018e+00   0.06738     1.629
Med.Statin.LLDyes                                         5.797e-01  1.725e+00   0.22937     1.465
Med.all.antiplateletyes                                   3.272e-01  3.056e+00   0.10249     1.045
GFR_MDRD                                                  9.994e-01  1.001e+00   0.99867     1.000
BMI                                                       1.001e+00  9.992e-01   0.99922     1.002
MedHx_CVDNo                                               4.526e-01  2.209e+00   0.14242     1.438
stenose50-70%                                             2.025e+08  4.938e-09   0.00000       Inf
stenose70-90%                                             1.880e+08  5.319e-09   0.00000       Inf
stenose90-99%                                             2.049e+08  4.880e-09   0.00000       Inf
stenose100% (Occlusion)                                   1.237e+01  8.084e-02   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             9.479e+15  1.055e-16   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.862  (se = 0.031 )
Likelihood ratio test= 46.37  on 31 df,   p=0.04
Wald test            = 23.26  on 31 df,   p=0.8
Score (logrank) test = 42.37  on 31 df,   p=0.08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HDAC9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.hdac9 '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.hdac9 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: HDAC9 
Effect size...............: -0.417773 
Standard error............: 0.476158 
Odds ratio (effect size)..: 0.659 
Lower 95% CI..............: 0.259 
Upper 95% CI..............: 1.674 
T-value...................: -0.877382 
P-value...................: 0.3802791 
Sample size in model......: 530 
Number of events..........: 23 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

30-days follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colnames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")

write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

90-days follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

Correlations

We correlated plaque levels of the biomarkers.

Plaque HDAC9 expression levels


# Installation of ggcorrplot()
# --------------------------------
# if(!require(devtools)) 
#   install.packages("devtools")
# devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)
Loading required package: ggplot2

# Creating matrix - inverse-rank transformation
# --------------------------------
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
                          select = c("HDAC9",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AERNASE.clin.hdac9.temp$CalcificationPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CalcificationPlaque)
AERNASE.clin.hdac9.temp$CollagenPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CollagenPlaque)
AERNASE.clin.hdac9.temp$Fat10Perc <- as.numeric(AERNASE.clin.hdac9.temp$Fat10Perc)
AERNASE.clin.hdac9.temp$MAC_binned <- as.numeric(AERNASE.clin.hdac9.temp$MAC_binned)
AERNASE.clin.hdac9.temp$SMC_binned <- as.numeric(AERNASE.clin.hdac9.temp$SMC_binned)
AERNASE.clin.hdac9.temp$IPH <- as.numeric(AERNASE.clin.hdac9.temp$IPH)
AERNASE.clin.hdac9.temp$Symptoms.5G <- as.numeric(AERNASE.clin.hdac9.temp$Symptoms.5G)
AERNASE.clin.hdac9.temp$AsymptSympt <- as.numeric(AERNASE.clin.hdac9.temp$AsymptSympt)
AERNASE.clin.hdac9.temp$EP_major <- as.numeric(AERNASE.clin.hdac9.temp$EP_major)
AERNASE.clin.hdac9.temp$EP_composite <- as.numeric(AERNASE.clin.hdac9.temp$EP_composite)
str(AERNASE.clin.hdac9.temp)
'data.frame':   611 obs. of  15 variables:
 $ HDAC9                 : int  24 11 7 5 13 46 14 0 30 12 ...
 $ CalcificationPlaque   : num  2 2 2 1 1 1 2 2 2 1 ...
 $ CollagenPlaque        : num  2 2 2 2 2 2 1 2 2 2 ...
 $ Fat10Perc             : num  2 2 2 2 1 1 2 1 2 1 ...
 $ IPH                   : num  1 2 2 1 1 1 1 2 1 1 ...
 $ MAC_binned            : num  2 1 2 1 1 2 2 1 2 1 ...
 $ SMC_binned            : num  2 2 2 2 2 2 1 2 2 2 ...
 $ MAC_rankNorm          : num  -0.0697 -0.7511 0.9114 -0.38 -1.0968 ...
 $ SMC_rankNorm          : num  1.522 0.58 0.673 1.256 0.342 ...
 $ MAC_SMC_ratio_rank    : num  -0.215 -0.88 0.728 -0.36 -1.31 ...
 $ VesselDensity_rankNorm: num  -0.723 0.769 0.854 1.14 -0.861 ...
 $ Symptoms.5G           : num  4 4 1 4 2 4 5 5 5 5 ...
 $ AsymptSympt           : num  2 2 1 2 1 2 2 2 2 2 ...
 $ EP_major              : num  1 1 1 1 2 2 2 1 1 1 ...
 $ EP_composite          : num  1 2 2 1 2 2 2 1 1 1 ...
AERNASE.clin.hdac9.matrix.RANK <- as.matrix(AERNASE.clin.hdac9.temp)
rm(AERNASE.clin.hdac9.temp)
corr_biomarkers.rank <- round(cor(AERNASE.clin.hdac9.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
corr_biomarkers.rank
                        HDAC9 CalcificationPlaque CollagenPlaque Fat10Perc    IPH MAC_binned SMC_binned MAC_rankNorm SMC_rankNorm MAC_SMC_ratio_rank
HDAC9                   1.000               0.044         -0.075     0.104  0.051      0.044     -0.095       -0.005       -0.153             -0.091
CalcificationPlaque     0.044               1.000          0.126     0.023  0.084     -0.079      0.057       -0.008        0.008              0.036
CollagenPlaque         -0.075               0.126          1.000    -0.077  0.002      0.010      0.336        0.016        0.305              0.096
Fat10Perc               0.104               0.023         -0.077     1.000  0.204      0.294     -0.157        0.196       -0.253             -0.014
IPH                     0.051               0.084          0.002     0.204  1.000      0.094     -0.118        0.135       -0.086             -0.025
MAC_binned              0.044              -0.079          0.010     0.294  0.094      1.000      0.043        0.628       -0.040             -0.052
SMC_binned             -0.095               0.057          0.336    -0.157 -0.118      0.043      1.000        0.110        0.557              0.083
MAC_rankNorm           -0.005              -0.008          0.016     0.196  0.135      0.628      0.110        1.000        0.213             -0.027
SMC_rankNorm           -0.153               0.008          0.305    -0.253 -0.086     -0.040      0.557        0.213        1.000              0.176
MAC_SMC_ratio_rank     -0.091               0.036          0.096    -0.014 -0.025     -0.052      0.083       -0.027        0.176              1.000
VesselDensity_rankNorm -0.031              -0.045          0.169     0.023  0.200      0.077      0.115        0.088        0.083              0.023
Symptoms.5G            -0.070               0.027         -0.029     0.059  0.027     -0.011     -0.018        0.015        0.015             -0.004
AsymptSympt            -0.024              -0.025         -0.077     0.126  0.044      0.017     -0.097        0.029       -0.067             -0.005
EP_major               -0.062               0.059          0.045    -0.021  0.006     -0.029      0.077       -0.002        0.032             -0.009
EP_composite           -0.029               0.066          0.040    -0.041  0.071     -0.063      0.028       -0.014        0.028              0.032
                       VesselDensity_rankNorm Symptoms.5G AsymptSympt EP_major EP_composite
HDAC9                                  -0.031      -0.070      -0.024   -0.062       -0.029
CalcificationPlaque                    -0.045       0.027      -0.025    0.059        0.066
CollagenPlaque                          0.169      -0.029      -0.077    0.045        0.040
Fat10Perc                               0.023       0.059       0.126   -0.021       -0.041
IPH                                     0.200       0.027       0.044    0.006        0.071
MAC_binned                              0.077      -0.011       0.017   -0.029       -0.063
SMC_binned                              0.115      -0.018      -0.097    0.077        0.028
MAC_rankNorm                            0.088       0.015       0.029   -0.002       -0.014
SMC_rankNorm                            0.083       0.015      -0.067    0.032        0.028
MAC_SMC_ratio_rank                      0.023      -0.004      -0.005   -0.009        0.032
VesselDensity_rankNorm                  1.000      -0.088      -0.101    0.119        0.171
Symptoms.5G                            -0.088       1.000       0.884    0.018        0.053
AsymptSympt                            -0.101       0.884       1.000    0.016        0.036
EP_major                                0.119       0.018       0.016    1.000        0.685
EP_composite                            0.171       0.053       0.036    0.685        1.000
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AERNASE.clin.hdac9.matrix.RANK, use = "pairwise.complete.obs", method = "spearman", exact = FALSE)
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

library(data.table)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.2 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

Attaching package: ‘data.table’

The following object is masked from ‘package:SummarizedExperiment’:

    shift

The following object is masked from ‘package:GenomicRanges’:

    shift

The following object is masked from ‘package:IRanges’:

    shift

The following objects are masked from ‘package:S4Vectors’:

    first, second
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
NA
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
Installing package into ‘/Users/swvanderlaan/Library/R/x86_64/4.1/library’
(as ‘lib’ is unspecified)
also installing the dependencies ‘RPEIF’, ‘RPEGLMEN’, ‘pyinit’, ‘googleVis’, ‘RPESE’, ‘RobStatTM’

trying URL 'https://cloud.r-project.org/bin/macosx/contrib/4.1/RPEIF_1.2.4.tgz'
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trying URL 'https://cloud.r-project.org/bin/macosx/contrib/4.1/RPEGLMEN_1.1.1.tgz'
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downloaded 1.2 MB

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trying URL 'https://cloud.r-project.org/bin/macosx/contrib/4.1/PerformanceAnalytics_2.0.4.tgz'
Content type 'application/x-gzip' length 3155393 bytes (3.0 MB)
==================================================
downloaded 3.0 MB

The downloaded binary packages are in
    /var/folders/cj/1vxt4xb11m1cx7wn020f8hww0000gn/T//Rtmpj0vR9W/downloaded_packages
Loading required package: PerformanceAnalytics
Loading required package: xts
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric


Attaching package: ‘xts’

The following objects are masked from ‘package:data.table’:

    first, last

The following object is masked from ‘package:S4Vectors’:

    first


Attaching package: ‘PerformanceAnalytics’

The following object is masked from ‘package:graphics’:

    legend
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AERNASE.clin.hdac9.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)

# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AERNASE.clin.hdac9,
        columns = c("HDAC9", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("HDAC9",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Session information


Version:      v1.0.0
Last update:  2021-03-18
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse HDAC9 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_


_W_


**Changes log**

* v1.0.0 Inital version.

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] GGally_2.1.2                PerformanceAnalytics_2.0.4  xts_0.12.1                  zoo_1.8-9                   data.table_1.14.2          
 [6] ggcorrplot_0.1.3.999        ggplot2_3.3.5               rmarkdown_2.11              worcs_0.1.9.1               SummarizedExperiment_1.24.0
[11] Biobase_2.54.0              GenomicRanges_1.46.1        GenomeInfoDb_1.30.0         IRanges_2.28.0              S4Vectors_0.32.3           
[16] BiocGenerics_0.40.0         MatrixGenerics_1.6.0        matrixStats_0.61.0         

loaded via a namespace (and not attached):
  [1] backports_1.4.1          BiocFileCache_2.2.0      plyr_1.8.6               lazyeval_0.2.2           BiocParallel_1.28.3      crosstalk_1.2.0         
  [7] usethis_2.1.5            digest_0.6.29            ensembldb_2.18.2         htmltools_0.5.2          fansi_1.0.2              magrittr_2.0.2          
 [13] memoise_2.0.1            gert_1.5.0               remotes_2.4.2            credentials_1.3.2        Biostrings_2.62.0        askpass_1.1             
 [19] prettyunits_1.1.1        colorspace_2.0-3         rappdirs_0.3.3           blob_1.2.2               xfun_0.29                dplyr_1.0.7             
 [25] callr_3.7.0              crayon_1.5.0             RCurl_1.98-1.5           jsonlite_1.7.2           glue_1.6.2               prereg_0.5.0            
 [31] gtable_0.3.0             zlibbioc_1.40.0          XVector_0.34.0           DelayedArray_0.20.0      car_3.0-12               pkgbuild_1.3.1          
 [37] abind_1.4-5              scales_1.1.1             DBI_1.1.2                rstatix_0.7.0            Rcpp_1.0.8.3             progress_1.2.2          
 [43] bit_4.0.4                DT_0.20                  htmlwidgets_1.5.4        httr_1.4.2               RColorBrewer_1.1-2       ellipsis_0.3.2          
 [49] pkgconfig_2.0.3          reshape_0.8.8            XML_3.99-0.8             farver_2.1.0             sass_0.4.0               dbplyr_2.1.1            
 [55] utf8_1.2.2               tidyselect_1.1.1         labeling_0.4.2           rlang_1.0.2              reshape2_1.4.4           AnnotationDbi_1.56.2    
 [61] munsell_0.5.0            tools_4.1.2              cachem_1.0.6             cli_3.2.0                generics_0.1.1           RSQLite_2.2.9           
 [67] ranger_0.13.1            devtools_2.4.3           broom_0.7.11             evaluate_0.14            stringr_1.4.0            fastmap_1.1.0           
 [73] yaml_2.2.1               sys_3.4                  rticles_0.22             processx_3.5.2           knitr_1.37               bit64_4.0.5             
 [79] fs_1.5.2                 purrr_0.3.4              AnnotationFilter_1.18.0  KEGGREST_1.34.0          xml2_1.3.3               biomaRt_2.50.2          
 [85] compiler_4.1.2           rstudioapi_0.13          filelock_1.0.2           curl_4.3.2               png_0.1-7                testthat_3.1.1          
 [91] ggsignif_0.6.3           tibble_3.1.6             bslib_0.3.1              stringi_1.7.6            ps_1.6.0                 method
 [97] desc_1.4.0               lattice_0.20-45          ProtGenerics_1.26.0      Matrix_1.4-0             vctrs_0.3.8              pillar_1.7.0            
[103] lifecycle_1.0.1          jquerylib_0.1.4          bitops_1.0-7             rtracklayer_1.54.0       R6_2.5.1                 BiocIO_1.4.0            
[109] sessioninfo_1.2.2        assertthat_0.2.1         pkgload_1.2.4            openssl_1.4.6            rprojroot_2.0.2          rjson_0.2.21            
[115] withr_2.5.0              GenomicAlignments_1.30.0 Rsamtools_2.10.0         GenomeInfoDbData_1.2.7   parallel_4.1.2           hms_1.1.1               
[121] quadprog_1.5-8           grid_4.1.2               tidyr_1.1.4              carData_3.0-5            ggpubr_0.4.0             restfulr_0.0.13         

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com swvanderlaan.github.io.
---
title: "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: "Athero-Express RNA sequencing preparation"
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---
# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan"

### MacBook Air 
# ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")



setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

install.packages.auto("GGally")
install.packages.auto("ggcorrplot")

# for Seurat etc
install.packages.auto("GenomicFeatures")
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
install.packages.auto("DESeq2")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
install.packages.auto("EnhancedVolcano")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```

# Main analyses

Here we perform the main analyses. We first load the prepared RNAseq data, and extract only the relevant genes. 

## Targets

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes

```


# Loading data

We simply load the previously saved `RDS`-file and extract the clinical and RNAseq data from that.

```{r LoadAEDB}
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)

AERNASE <- readRDS(file = paste0(OUT_loc, "/20220318.AERNA.CEA.611pts.SE.after_qc.IC_academic.RDS"))

```


```{r}
AERNASE.clin <- as_tibble(colData(AERNASE))

AERNASE.hdac9 <- subset(AERNASE, subset = (rowRanges(AERNASE)$symbol %in% target_genes))

row.names(AERNASE.hdac9) <- rowData(AERNASE.hdac9)$symbol
temp <- as_tibble(t(assay(AERNASE.hdac9)), rownames = "STUDY_NUMBER")

AERNASE.clin.hdac9 <- as.data.frame(merge(AERNASE.clin, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE))
rm(temp)

```

Getting some summary statistics.
```{r}
cat("Average expression of a random selection of 1000 genes.\n")
set.seed(141619)
mean(rowMeans(as_tibble(assay(AERNASE)) %>% dplyr::sample_n(1000)))

cat("\nAverage expression of target genes.\n")
rowMeans(assay(AERNASE.hdac9))

cat("\nGene information.\n")
rowRanges(AERNASE.hdac9)

AERNASE.hdac9.genedata <- as_tibble(rowRanges(AERNASE.hdac9))

AERNASE.hdac9.genedata
```


# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

### Quantitative traits

We inspect the plaque characteristics, and `inverse-rank normal transformation` continuous phenotypes.

```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$MAC_rankNorm)

min_macmean <- min(AERNASE.clin.hdac9$MAC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$SMC_rankNorm)

min_smcmean <- min(AERNASE.clin.hdac9$SMC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

ggpubr::gghistogram(AERNASE.clin.hdac9, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$VesselDensity_rankNorm)

min_vesseldensity <- min(AERNASE.clin.hdac9$VesselDensity_rankNorm, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

ggpubr::gghistogram(AERNASE.clin.hdac9, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed ('unstable') as compared to 'stable'. 

```{r CrossSec: plaques - transformations and visualisations continuous MAC-SMC}

AERNASE.clin.hdac9$MAC_SMC_ratio <- AERNASE.clin.hdac9$MAC_rankNorm / AERNASE.clin.hdac9$SMC_rankNorm

AERNASE.clin.hdac9$MAC_SMC_ratio_rank  <- qnorm((rank(AERNASE.clin.hdac9$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9$MAC_SMC_ratio)))


cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$MAC_rankNorm)
summary(AERNASE.clin.hdac9$SMC_rankNorm)
summary(AERNASE.clin.hdac9$MAC_SMC_ratio_rank)

ggpubr::gghistogram(AERNASE.clin.hdac9, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())

```


### Binary traits
```{r CrossSec: plaques - transformations and visualisations binary}
library(dplyr)
# calcification
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Calc.bin)
contrasts(AERNASE.clin.hdac9$Calc.bin)

AERNASE.clin.hdac9$CalcificationPlaque <- as.factor(AERNASE.clin.hdac9$Calc.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CalcificationPlaque)) %>%
  dplyr::group_by(Gender, CalcificationPlaque) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Collagen.bin)
contrasts(AERNASE.clin.hdac9$Collagen.bin)

AERNASE.clin.hdac9$CollagenPlaque <- as.factor(AERNASE.clin.hdac9$Collagen.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(CollagenPlaque)) %>%
  dplyr::group_by(Gender, CollagenPlaque) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Fat.bin_10)
contrasts(AERNASE.clin.hdac9$Fat.bin_10)

AERNASE.clin.hdac9$Fat10Perc <- as.factor(AERNASE.clin.hdac9$Fat.bin_10)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(Fat10Perc)) %>%
  dplyr::group_by(Gender, Fat10Perc) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$Macrophages.bin)
contrasts(AERNASE.clin.hdac9$Macrophages.bin)

AERNASE.clin.hdac9$MAC_binned <- as.factor(AERNASE.clin.hdac9$Macrophages.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(MAC_binned)) %>%
  dplyr::group_by(Gender, MAC_binned) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)



# SMC binned
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$SMC.bin)
contrasts(AERNASE.clin.hdac9$SMC.bin)

AERNASE.clin.hdac9$SMC_binned <- as.factor(AERNASE.clin.hdac9$SMC.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(SMC_binned)) %>%
  dplyr::group_by(Gender, SMC_binned) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)




# IPH
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$IPH.bin)
contrasts(AERNASE.clin.hdac9$IPH.bin)

AERNASE.clin.hdac9$IPH <- as.factor(AERNASE.clin.hdac9$IPH.bin)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(IPH)) %>%
  dplyr::group_by(Gender, IPH) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AERNASE.clin.hdac9$AsymptSympt)
contrasts(AERNASE.clin.hdac9$AsymptSympt)

AERNASE.clin.hdac9$AsymptSympt <- as.factor(AERNASE.clin.hdac9$AsymptSympt)

df <- AERNASE.clin.hdac9 %>%
  dplyr::filter(!is.na(AsymptSympt)) %>%
  dplyr::group_by(Gender, AsymptSympt) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

### Correlations between `r TRAIT_OF_INTEREST` plaque levels and surgery year

Here we compare the _`r TRAIT_OF_INTEREST`_ plaque gene expression levels.

```{r scatters: year of surgery}
p1 <- ggpubr::ggscatter(AERNASE.clin.hdac9, 
                        x = "ORyear", 
                        y = "HDAC9",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman", 
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "HDAC9 (normalized expression)",
                        ggtheme = theme_minimal())

p1 

rm(p1)

```



## Prepare modeling

In this section we make some variables to assist with analysis.

We fix the *diabetes* status variable.

```{r FixDiabetes, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"DiabetesStatus"] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == -999] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == 1] <- "Control (no Diabetes Dx/Med)"
AEDB.CEA$DiabetesStatus[DM.composite == 2] <- "Diabetes"
detach(AEDB.CEA)

table(AEDB.CEA$DM.composite, AEDB.CEA$DiabetesStatus)
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$DiabetesStatus <- to_factor(AEDB.CEA.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}
# AEDB.CEA$CAD_history
# AEDB.CEA$Stroke_history
# AEDB.CEA$Peripheral.interv

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"MedHx_CVD"] <- NA
AEDB.CEA$MedHx_CVD[CAD_history == "No history CAD" | Stroke_history == 1 | Peripheral.interv == "no"] <- "No"
AEDB.CEA$MedHx_CVD[CAD_history == "History CAD" | Stroke_history == 2 | Peripheral.interv == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$CAD_history)
table(AEDB.CEA$Stroke_history)
table(AEDB.CEA$Peripheral.interv)
table(AEDB.CEA$MedHx_CVD)

# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$AlcoholUse <- to_factor(AEDB.CEA.temp$AlcoholUse)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)


```


```{r}
AERNASE.clin.hdac9$DiabetesStatus <- NULL
AERNASE.clin.hdac9$MedHx_CVD <- NULL

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "dateok", "DiabetesStatus", "MedHx_CVD")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.hdac9$MedHx_CVD <- as_factor(AERNASE.clin.hdac9$MedHx_CVD)
AERNASE.clin.hdac9$DiabetesStatus <- as_factor(AERNASE.clin.hdac9$DiabetesStatus)

```


```{r CrossSec: plaques - setup regression }

study.samplesize = nrow(AERNASE.clin.hdac9) # study.samplesize is an expected variable in the GLM.BIN() GLM.CON() functions

TRAITS.TARGET.RANK = c("HDAC9")

TRAITS.CON.RANK = c("MAC_rankNorm", "SMC_rankNorm", "MAC_SMC_ratio_rank", "VesselDensity_rankNorm")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AERNASE.clin.hdac9$ORdate_epoch <- as.numeric(AERNASE.clin.hdac9$dateok)
AERNASE.clin.hdac9$ORdate_year <- AERNASE.clin.hdac9$ORyear

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
summary(AERNASE.clin.hdac9$ORdate_epoch)

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
table(AERNASE.clin.hdac9$ORdate_year)

# COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")
str(AERNASE.clin.hdac9)
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

```{r}
detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

```

Analysis of continuous/quantitative plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, paged.print=FALSE}
library(dplyr)
library(MASS)

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))

cat("Running linear regression...\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year,
    #           data  =  currentDF, family = binomial(link = "logit"))
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch,
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the `inverse-rank normalized` data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
              Hypertension.composite + DiabetesStatus + SmokerStatus + 
              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
              MedHx_CVD + stenose, 
              data = currentDF) 
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque `r TRAIT_OF_INTEREST` expression levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK}
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, family = binomial(link = "logit"))

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #              Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #              MedHx_CVD + stenose, 
    #            data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.hdac9", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           rowNames = FALSE, colNmes = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque `r TRAIT_OF_INTEREST` expression levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)
```



```{r}

AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "max.followup", 
                                                        "FU.cutt.off.3years", "FU.cutt.off.30days", "FU.cutt.off.90days",
                                                        "ep_major_t_3years", "ep_stroke_t_3years", "ep_coronary_t_3years",
                                                        "ep_cvdeath_t_3years",
                                                        "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years"
                                                        )), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE)


```


```{r Calculate new FU cut-offs: maximum FU}


AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times, message=FALSE, warning=FALSE}

attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"epmajor.30days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epstroke.30days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcoronary.30days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.30days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AERNASE.clin.hdac9[,"epmajor.90days"] <- AERNASE.clin.hdac9$epmajor.3years
AERNASE.clin.hdac9$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epstroke.90days"] <- AERNASE.clin.hdac9$epstroke.3years
AERNASE.clin.hdac9$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcoronary.90days"] <- AERNASE.clin.hdac9$epcoronary.3years
AERNASE.clin.hdac9$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AERNASE.clin.hdac9[,"epcvdeath.90days"] <- AERNASE.clin.hdac9$epcvdeath.3years
AERNASE.clin.hdac9$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AERNASE.clin.hdac9)

AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9,  select = c("STUDY_NUMBER", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AERNASE.clin.hdac9.temp$Gender <- to_factor(AERNASE.clin.hdac9.temp$Gender)
AERNASE.clin.hdac9.temp$Hospital <- to_factor(AERNASE.clin.hdac9.temp$Hospital)
AERNASE.clin.hdac9.temp$Artery_summary <- to_factor(AERNASE.clin.hdac9.temp$Artery_summary)

DT::datatable(AERNASE.clin.hdac9.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.hdac9.", rownames = FALSE)

rm(AERNASE.clin.hdac9.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, target_of_interest){
  cat("Summarizing Cox regression results for '", target_of_interest ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(target_of_interest, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, target_of_interest, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", target_of_interest, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.hdac9[,events]))
  print(table(AERNASE.clin.hdac9[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.hdac9[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.hdac9, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.hdac9.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)


```


#### 30-days follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colnames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")

write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)


```


#### 90-days follow-up

##### Model 1 
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.hdac9)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.hdac9.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.hdac9.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.hdac9", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.hdac9.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.hdac9.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.hdac9.COX.results)


```


# Correlations
We correlated plaque levels of the biomarkers.

## Plaque `r TRAIT_OF_INTEREST` expression levels

```{r}

# Installation of ggcorrplot()
# --------------------------------
# if(!require(devtools)) 
#   install.packages("devtools")
# devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

```


```{r}

# Creating matrix - inverse-rank transformation
# --------------------------------
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AERNASE.clin.hdac9.temp <- subset(AERNASE.clin.hdac9, 
                          select = c("HDAC9",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AERNASE.clin.hdac9.temp$CalcificationPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CalcificationPlaque)
AERNASE.clin.hdac9.temp$CollagenPlaque <- as.numeric(AERNASE.clin.hdac9.temp$CollagenPlaque)
AERNASE.clin.hdac9.temp$Fat10Perc <- as.numeric(AERNASE.clin.hdac9.temp$Fat10Perc)
AERNASE.clin.hdac9.temp$MAC_binned <- as.numeric(AERNASE.clin.hdac9.temp$MAC_binned)
AERNASE.clin.hdac9.temp$SMC_binned <- as.numeric(AERNASE.clin.hdac9.temp$SMC_binned)
AERNASE.clin.hdac9.temp$IPH <- as.numeric(AERNASE.clin.hdac9.temp$IPH)
AERNASE.clin.hdac9.temp$Symptoms.5G <- as.numeric(AERNASE.clin.hdac9.temp$Symptoms.5G)
AERNASE.clin.hdac9.temp$AsymptSympt <- as.numeric(AERNASE.clin.hdac9.temp$AsymptSympt)
AERNASE.clin.hdac9.temp$EP_major <- as.numeric(AERNASE.clin.hdac9.temp$EP_major)
AERNASE.clin.hdac9.temp$EP_composite <- as.numeric(AERNASE.clin.hdac9.temp$EP_composite)
str(AERNASE.clin.hdac9.temp)
AERNASE.clin.hdac9.matrix.RANK <- as.matrix(AERNASE.clin.hdac9.temp)
rm(AERNASE.clin.hdac9.temp)

```

```{r CrossSampleType Correlations}
corr_biomarkers.rank <- round(cor(AERNASE.clin.hdac9.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AERNASE.clin.hdac9.matrix.RANK, use = "pairwise.complete.obs", method = "spearman", exact = FALSE)
```

```{r}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```

```{r Correlations table}
library(data.table)
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

```

```{r Correlations alternative visual 1, message=FALSE, warning=FALSE}
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AERNASE.clin.hdac9.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
```


```{r Correlations alternative visual 2, message=FALSE, warning=FALSE}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AERNASE.clin.hdac9,
        columns = c("HDAC9", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("HDAC9",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.0
    Last update:  2021-03-18
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse HDAC9 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_
    
    _C_
    
    
    _W_
    
    
    **Changes log**

    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
```

+---------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
+---------------------------------------------------------------------------------------------------------------------------------------+


